{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp callback.MVP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MVP (aka TSBERT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Self-Supervised Pretraining of Time Series Models\n",
    "\n",
    "Masked Value Predictor callback used to predict time series step values after a binary mask has been applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from torch.distributions.geometric import Geometric\n",
    "from torch.distributions.binomial import Binomial\n",
    "from tsai.imports import *\n",
    "from fastai.callback.all import *\n",
    "from tsai.utils import *\n",
    "from tsai.models.utils import *\n",
    "from tsai.models.layers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "def create_subsequence_mask(o, r=.15, lm=3, stateful=True, sync=False):\n",
    "    if r <= 0: return torch.zeros_like(o).bool()\n",
    "    device = o.device\n",
    "    if o.ndim == 2: o = o[None]\n",
    "    n_masks, mask_dims, mask_len = o.shape\n",
    "    if sync == 'random': sync = random.random() > .5\n",
    "    dims = 1 if sync else mask_dims\n",
    "    if stateful: \n",
    "        numels = n_masks * dims * mask_len\n",
    "        pm = torch.tensor([1 / lm], device=device)\n",
    "        pu = torch.clip(pm * (r / max(1e-6, 1 - r)), 1e-3, 1)\n",
    "        zot, proba_a, proba_b = (torch.as_tensor([False, True], device=device), pu, pm) if random.random() > pm else \\\n",
    "        (torch.as_tensor([True, False], device=device), pm, pu)\n",
    "        max_len = max(1, 2 * torch.div(numels, (1/pm + 1/pu), rounding_mode='floor').long().item())\n",
    "        for i in range(10):\n",
    "            _dist_a = (Geometric(probs=proba_a).sample([max_len])+1).long()\n",
    "            _dist_b = (Geometric(probs=proba_b).sample([max_len])+1).long()\n",
    "            dist_a = _dist_a if i == 0 else torch.cat((dist_a, _dist_a), dim=0)\n",
    "            dist_b = _dist_b if i == 0 else torch.cat((dist_b, _dist_b), dim=0)\n",
    "            add = torch.add(dist_a, dist_b)\n",
    "            if torch.gt(torch.sum(add), numels): break\n",
    "        dist_len = torch.argmax((torch.cumsum(add, 0) >= numels).float()) + 1\n",
    "        if dist_len%2: dist_len += 1\n",
    "        repeats = torch.cat((dist_a[:dist_len], dist_b[:dist_len]), -1).flatten()\n",
    "        zot = zot.repeat(dist_len)\n",
    "        mask = torch.repeat_interleave(zot, repeats)[:numels].reshape(n_masks, dims, mask_len)\n",
    "    else: \n",
    "        probs = torch.tensor(r, device=device)\n",
    "        mask = Binomial(1, probs).sample((n_masks, dims, mask_len)).bool()\n",
    "    if sync: mask = mask.repeat(1, mask_dims, 1)\n",
    "    return mask\n",
    "\n",
    "def create_variable_mask(o, r=.15):\n",
    "    if r <= 0: return torch.zeros_like(o).bool()\n",
    "    device = o.device\n",
    "    n_masks, mask_dims, mask_len = o.shape\n",
    "    _mask = torch.zeros((n_masks * mask_dims, mask_len), device=device)\n",
    "    if int(mask_dims * r) > 0:\n",
    "        n_masked_vars = int(n_masks * mask_dims * r)\n",
    "        p = torch.tensor([1./(n_masks * mask_dims)], device=device).repeat([n_masks * mask_dims])\n",
    "        sel_dims = p.multinomial(num_samples=n_masked_vars, replacement=False)\n",
    "        _mask[sel_dims] = 1\n",
    "    mask = _mask.reshape(*o.shape).bool()\n",
    "    return mask\n",
    "\n",
    "def create_future_mask(o, r=.15, sync=False):\n",
    "    if r <= 0: return torch.zeros_like(o).bool()\n",
    "    if o.ndim == 2: o = o[None]\n",
    "    n_masks, mask_dims, mask_len = o.shape\n",
    "    if sync == 'random': sync = random.random() > .5\n",
    "    dims = 1 if sync else mask_dims\n",
    "    probs = torch.tensor(r, device=o.device)\n",
    "    mask = Binomial(1, probs).sample((n_masks, dims, mask_len))\n",
    "    if sync: mask = mask.repeat(1, mask_dims, 1)\n",
    "    mask = torch.sort(mask,dim=-1, descending=False)[0].bool()\n",
    "    return mask\n",
    "\n",
    "def self_mask(o): \n",
    "    mask1 = torch.isnan(o)\n",
    "    mask2 = rotate_axis0(mask1)\n",
    "    return torch.logical_and(mask2, ~mask1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.rand(16, 3, 100)\n",
    "mask = create_subsequence_mask(t, sync=False)\n",
    "test_eq(mask.shape, t.shape)\n",
    "mask = create_subsequence_mask(t, sync=True)\n",
    "test_eq(mask.shape, t.shape)\n",
    "mask = create_variable_mask(t)\n",
    "test_eq(mask.shape, t.shape)\n",
    "mask = create_future_mask(t)\n",
    "test_eq(mask.shape, t.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = torch.randn(2, 3, 4)\n",
    "o[o>.5] = np.nan\n",
    "test_eq(torch.isnan(self_mask(o)).sum(), 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IqVy5sts+uHhf5rr4/OHpHDh8+HC3ehcm9/zxzTffuKblHpuhoaFu++Wdd97Jcy4uahueOXPGZGdnu617165dxm63ux0Duef7xo0bm6ysLNf0N99800gymzdvzlP2wuPKkyNHjng81owxZtKkSUaS2bp1a4HLuFC3bt3cztcX2rNnj+nSpYuZMmWK+fTTT80bb7xh4uLiTIUKFczChQsLXXbnzp1NeHi4+e2334pcH+BC3CaHSyoiIkJr1qzRr7/+mm+ZC399PnXqlI4ePao2bdrIGOPxdoN7773XbfmNGjVSlSpV1LdvX9f0Ro0aKSIiwmMWnPvvv9/tisEDDzygihUr6t///ne+dZw9e7YaN26shIQEHT161PW64YYbJP1x64+/5N5Tv3DhwiL9ql1UI0aMcP1ts9k0YsQInT171nVbVkREhE6dOlXgbVWzZ89Wu3btVK1aNbf90rlzZ2VnZ+vLL7+U9McDyxUrVnT7JT8oKEgPPvhgies/e/ZsORwO3XjjjW7rTkxMVFhYWJ42adKkidq1a+f6v2bNmmrUqJFbH/nkk0/UokWLPL+2S3JdNfRFXwgLC1O/fv1c/+f218aNG6tVq1au6bl/X1jHC4+Xc+fO6dixY2rQoIEiIiLcbj+NiIjQf//7X23fvr3Q+nzxxRdKSkpSp06dNHfuXNnt9kLnOXbsmKpVq5ZnekREhH755RetXbs233kHDBigrKwst1vJPvroI50/f97jMwQXP5fUrl27PO1ms9lct15d6MKrva+++qqWLFlS6Ovxxx/Ps5xbbrklT7mkpCRJ7m3y22+/yel0ql27dm7t4UlhbXT8+HEtX75cffv21YkTJ1x97dixY0pKStL27du1f/9+t3ly26Qot0UVRXZ2thYvXqyePXsqLi7ONb1x48au7c81d+5c5eTkqG/fvm7HRkxMjBo2bFikY+PCfZm7ze3atXPd7lVamjRp4nbFMvfYvOGGG9z2y8XHbHHa0G63u64mZWdn69ixYwoLC1OjRo089qVBgwYpODjY9X/u+e3CY6Njx44yxhSaLCL3tnRPx35ISIhbGW/FxcVp8eLFGjp0qHr06KGHH35YGzZsUM2aNfXoo48WOO+LL76opUuX6qWXXuJ5M5QYt8nhkho/frxSUlJUp04dJSYm6qabbtKAAQNUv359V5m9e/fqmWee0aeffprnWQ+n0+n2f+6zABdyOByqXbt2nlvcHA6Hx2eBGjZs6PZ/WFiYatWqVWCq6+3bt2vLli151p0r92Fgf+jQoYN69eql1NRUvf766+rYsaN69uypO++8s0hfWj2pUKGCWxtIfzwgLsm1H4YNG6aPP/5YycnJuuyyy9SlSxf17dtXXbt2dc2zfft2bdq0qdD9smfPHtWqVSvPrUeNGjUqUf1z1+10OhUVFVXgunNd+IUlV7Vq1dz6yM6dO9WrV69C1+ttX8ivv9apUyfPNEludTx9+rTS0tI0bdo07d+/3+05rwuPl+eee0633HKL/vSnP6lZs2bq2rWr7r77btftSLnOnDmjbt26KTExUR9//LEqViz6x4S56BkzSXriiSe0dOlSXXPNNWrQoIG6dOmiO++8U23btnWVSUhI0NVXX60ZM2Zo8ODBkv64Re7aa6/Nk1nM0zHvqd1iY2MVGRlZYH0TExOLvG0Xq127dr4Pay9cuFAvvPCCNm7c6PbcWGHPohTWRjt27JAxRk8//bSefvppj8s4fPiwLrvsMtf/uW1S0LpPnz6d59yaX5rwI0eO6PTp03nOm9Ifx++FPyJt375dxhiPZSUV6XmS//73v/rLX/6i5cuXKzMz0+29i+t8KV18/sg9Ngs7ZovThrnPvU2ePFm7du1ye07KUya3i+uUGwgX5RnYi+UGoZ6ee8xNY+3P7ImRkZEaNGiQXnrpJf3yyy+qXbt2njIfffSR/vKXv2jw4MFuP6wBxUUwhEuqb9++ateunebNm6f//Oc/mjBhgl5++WXNnTtXycnJys7O1o033qjjx4/riSeeUEJCgqpUqaL9+/dr4MCBeR7CDwoK8rie/KZ7+rJWEjk5OWrevLlee+01j+9f/IHoSzabTXPmzNHq1av12WefafHixbrnnnv06quvavXq1QoLC8v3i8/FDx0XR1RUlDZu3KjFixfr888/1+eff65p06ZpwIABeu+99yT9sV9uvPFGj7+kS/8LsIrDZrN5bDdPD8xHRUVpxowZHpdz8RdoX/URX/QFb/rxgw8+qGnTpmnkyJFq3bq1HA6HbDab+vXr53a8tG/fXjt37tSCBQv0n//8R3/729/0+uuva+rUqW5XV+12u2666SYtWLBAixYtUvfu3Qutv/THlzNPX7oaN26sbdu2aeHChVq0aJE++eQTTZ48Wc8884xSU1Nd5QYMGKCHH35Yv/zyi7KysrR69Wq9/fbbRd4nJXH8+PF8H9a+UGhoqOtLbWG++uor3XzzzWrfvr0mT56sWrVqqVKlSpo2bVqeJC4XK6yNcttz9OjRea7C5Lo4eMxtk4Kep/joo480aNAgt2m+OFfm5OTIZrPp888/99huhQ3wm5GRoQ4dOig8PFzPPfec4uPjFRISou+++05PPPGEW/8u6Jznyz6Tq6THbHHa8MUXX9TTTz+te+65R88//7wiIyNVoUIFjRw5Ms9nYVHWXRyRkZGy2+06cOBAnvdypxUlXbY3cs+dx48fzxMMLVmyRAMGDFC3bt00depUv9YD5R/BEC65WrVqadiwYRo2bJgOHz6sq666SuPGjVNycrI2b96sn376Se+9957bGB6FZbzyxvbt293GRTl58qQOHDiQZ7yDC8XHx+v7779Xp06dSm1cg2uvvVbXXnutxo0bp5kzZ6p///6aNWuW7r33XtcvghkZGW7z7Nmzx+OycnJy9PPPP7sFKz/99JMkuWVsCw4OVo8ePdSjRw/l5ORo2LBheuedd/T000+rQYMGio+P18mTJwtNb1q3bl0tW7ZMJ0+edPtCtG3btjxlq1Wr5vH2xou3JT4+XkuXLlXbtm199otlfHx8oelgS7svzJkzRykpKXr11Vdd086cOZOn7aX//do6aNAgnTx5Uu3bt9ezzz7rFgzZbDbNmDFDt9xyi/r06aPPP/+8wGxcuRISEjRjxgw5nc48gUOVKlV0++236/bbb9fZs2d12223ady4cRozZozrlpt+/fpp1KhR+uc//6nTp0+rUqVKuv3220u0T+Lj47V48WIdP368wKtDt912m1auXFno8lJSUjxmYPTkk08+UUhIiBYvXux2pXbatGlFmr+gNsq9elupUqUipxDetWuXatSoke+VS0lKSkoq8jm2Zs2aCg0N9Xgr38XHb3x8vIwxqlevXol+CFmxYoWOHTumuXPnqn379q7pu3btylO2WrVqHvv8nj178lz1vtilPG6L04Zz5szR9ddfr7///e9u0zMyMvyeLKBChQpq3ry5x+yLa9asUf369f0+dlXuef/ivrtmzRrdeuutatmyZbGvXgOe8MwQLpns7Ow8tzVERUUpNjbWdSk+95etC3/JMsbkSd/sS3/961/dnr2ZMmWKzp8/r+Tk5Hzn6du3r/bv36933303z3unT5/WqVOn/FJX6Y9fei/+pe/Pf/6zpP/d0lC3bl0FBQW5ntHJNXny5HyXe+Gv8MYYvf3226pUqZI6deok6Y9nQi5UoUIF1+07uevt27ev0tPTtXjx4jzLz8jI0Pnz5yVJN910k86fP68pU6a43s/OztbEiRPzzBcfH6+tW7fqyJEjrmnff/+9vv76a7dyffv2VXZ2tp5//vk8yzh//rzHL0qF6dWrl77//nuPaZ9z26A0+4L0xzFzcX+YOHFinitnF7dfWFiYGjRo4PE2mODgYM2dO1dXX321evTooW+//bbQerRu3VrGGK1fv77A9QYHB6tJkyYyxrgddzVq1FBycrI+/PBDzZgxQ127di3xF75evXrJGON25SnXhfvKm2eG8hMUFCSbzea2/3fv3l2kjG6FtVFUVJQ6duyod955x+Mv9hceI7nWr1/v9myLJ7Vq1VLnzp3dXvkJCgpSUlKS5s+fr71797qmb9myJc9xf9tttykoKEipqal5+qgxJs/2elpXbtlcZ8+e9Xgei4+P1+rVq92u9C1cuNAthXd+qlSpIinvj0f+UJw29HRsz549O89zYcVRnNTavXv31tq1a90Com3btmn58uXq06ePW9mtW7e69Yfi8NRv9+/fr3/84x+64oorVKtWLdf0LVu2qFu3brr88su1cOHCgBvoGGUT4TQumRMnTqh27drq3bu3WrRoobCwMC1dulRr1651/aqdkJCg+Ph4jR49Wvv371d4eLg++eSTEt3zXFRnz55Vp06dXOlPJ0+erOuuu04333xzvvPcfffd+vjjjzV06FB98cUXatu2rbKzs7V161Z9/PHHWrx4sSt9rydOp9P1xT/3S/3bb7+tiIgIRUREuCUzuNh7772nyZMn69Zbb1V8fLxOnDihd999V+Hh4a6rWQ6HQ3369NHEiRNls9kUHx+vhQsX5vv8SkhIiBYtWqSUlBS1atVKn3/+uf71r3/pqaeecv0qd++99+r48eO64YYbVLt2be3Zs0cTJ07Un//8ZzVu3FiS9Nhjj+nTTz9V9+7dXWmqT506pc2bN2vOnDnavXu3atSooR49eqht27Z68skntXv3bjVp0kRz5871+AzAPffco9dee01JSUkaPHiwDh8+rKlTp6pp06ZuzxB06NBBQ4YMUVpamjZu3KguXbqoUqVK2r59u2bPnq0333xTvXv3zne/evLYY49pzpw56tOnj+655x4lJibq+PHj+vTTTzV16lS1aNHC677gre7du+uDDz6Qw+FQkyZNlJ6erqVLl+Z5pqBJkybq2LGjEhMTFRkZqXXr1rnS3HsSGhqqhQsX6oYbblBycrJWrlypZs2a5VuP6667TtWrV9fSpUtdySMkqUuXLoqJiVHbtm0VHR2tLVu26O2331a3bt3y/LI8YMAAVxt5CmqL6vrrr9fdd9+tt956S9u3b1fXrl2Vk5Ojr776Stdff71rm715Zig/3bp102uvvaauXbvqzjvv1OHDhzVp0iQ1aNBAmzZtKnDeorTRpEmTdN1116l58+a67777VL9+fR06dEjp6en65Zdf3MagOXz4sDZt2qThw4f7dBtTU1O1aNEitWvXTsOGDdP58+ddY0lduI3x8fF64YUXNGbMGO3evVs9e/ZU1apVtWvXLs2bN0/333+/Ro8ene962rRpo2rVqiklJUUPPfSQbDabPvjgA4+3fd17772aM2eOunbtqr59+2rnzp368MMPFR8fX+j25PaDhx56SElJSQoKCnJLaOJrRW3D7t2767nnntOgQYPUpk0bbd68WTNmzCj0SldBippaW/rjOdF3331X3bp10+jRo1WpUiW99tprio6OzpPYoHHjxurQoYPb2HabNm1yjZ20Y8cOOZ1OvfDCC5KkFi1aqEePHpKkxx9/XDt37lSnTp0UGxur3bt365133tGpU6fcfgg9ceKEkpKS9Ntvv+mxxx7Tv/71L7c6XJyGHyiyS5CxDjDGGJOVlWUee+wx06JFC1O1alVTpUoV06JFCzN58mS3cj/++KPp3LmzCQsLMzVq1DD33Xef+f777z2mhM5NA3yhDh06eEzlW7duXbe0tbmptVeuXGnuv/9+U61aNRMWFmb69+9vjh07lmeZF6d3Pnv2rHn55ZdN06ZNjd1uN9WqVTOJiYkmNTXVOJ3OAvdFbnpXT6/80o/m+u6778wdd9xh4uLijN1uN1FRUaZ79+5m3bp1buWOHDlievXqZSpXrmyqVatmhgwZYn744Yd89+POnTtNly5dTOXKlU10dLQZO3asW1rXOXPmmC5dupioqCgTHBxs4uLizJAhQ8yBAwfc1nvixAkzZswY06BBAxMcHGxq1Khh2rRpY1555RW3FObHjh0zd999twkPDzcOh8PcfffdZsOGDXnqZ4wxH374oalfv74JDg42f/7zn83ixYvzpNbO9de//tUkJiaa0NBQU7VqVdO8eXPz+OOPm19//dVV5uK+kMtTOx87dsyMGDHCXHbZZSY4ONjUrl3bpKSkmKNHj7rKeNMXitpfc0lyS0//22+/mUGDBpkaNWqYsLAwk5SUZLZu3Zon1fALL7xgrrnmGhMREWFCQ0NNQkKCGTdunFubeDqmjh49apo0aWJiYmLM9u3bC9yWhx56yDRo0MBt2jvvvGPat29vqlevbux2u4mPjzePPfaYx/2SlZVlqlWrZhwOhzl9+nSe9/M75nPTMl/o/PnzZsKECSYhIcEEBwebmjVrmuTkZLN+/foCt6EoLm6Di/397383DRs2NHa73SQkJJhp06Z5rGNJ2sgYY3bu3GkGDBhgYmJiTKVKlcxll11munfvbubMmeNWbsqUKaZy5comMzPT622+2MqVK01iYqIJDg429evXN1OnTvW4jcYY88knn5jrrrvOVKlSxVSpUsUkJCSY4cOHm23btrnK5Jda++uvvzbXXnutCQ0NNbGxsebxxx93pbG+eOiAV1991Vx22WXGbrebtm3bmnXr1hUptfb58+fNgw8+aGrWrGlsNluhabaLemxeuL4JEya4TS9KG545c8Y8+uijplatWiY0NNS0bdvWpKen59mm3HTZs2fP9rjuC7e1qKm1c+3bt8/07t3bhIeHm7CwMNO9e3eP5wFJec6duZ+xnl4X9vuZM2ea9u3bm5o1a5qKFSuaGjVqmFtvvTXPsVrQ5+bFywSKw2aMj54oB8qY3ME5165d69df7lF0u3fvVr169TRt2jSPo8kjsP38889KSEjQ559/7rq9sjjOnz+v2NhY9ejRI89zEii+K6+8Uh07dtTrr79e2lUBgIDFM0MAAJ+oX7++Bg8erJdeeqlE88+fP19HjhxxS56Cklm0aJG2b9+uMWPGlHZVACCg8cwQAMBnLkyKUVRr1qzRpk2b9Pzzz+vKK69Uhw4d/FAza+natatOnjxZ2tUAgIDHlSEAQKmaMmWKHnjgAUVFRen9998v7eoAACykWM8MTZkyRVOmTHGNSN+0aVM988wzrhTEZ86c0aOPPqpZs2YpKytLSUlJmjx5sqKjo/1SeQAAAAAoqWIFQ5999pmCgoLUsGFDGWP03nvvacKECdqwYYOaNm2qBx54QP/61780ffp0ORwOjRgxQhUqVMgzHggAAAAAlDavs8lFRkZqwoQJ6t27t2rWrKmZM2e6xonYunWrGjdurPT0dF177bU+qTAAAAAA+EKJEyhkZ2dr9uzZOnXqlFq3bq3169fr3LlzbiNXJyQkKC4ursBgKCsry20E9JycHB0/flzVq1eXzWYrafUAAAAAlHHGGJ04cUKxsbGqUMH36Q6KHQxt3rxZrVu31pkzZxQWFqZ58+apSZMm2rhxo4KDgxUREeFWPjo6WgcPHsx3eWlpaUpNTS12xQEAAABYw759+1S7dm2fL7fYwVCjRo20ceNGOZ1OzZkzRykpKVq5cmWJKzBmzBiNGjXK9b/T6VRcXJy0b58UHl7i5fqL01Hw+w6nd/P7U2F1AwBf8/acWVaV5c+Ksqq89qWyzKr9mL7oWYnPi5mZUp06qlq1qs/rJJUgGAoODlaDBg0kSYmJiVq7dq3efPNN3X777Tp79qwyMjLcrg4dOnRIMTEx+S7PbrfLbrfnfSM8PCCDoUJrVEiBUt2iwNudAMo5b8+ZZVWZ/qwoq9hpAceyTWLZDS+Yt+dFfz0+4/WNdzk5OcrKylJiYqIqVaqkZcuWud7btm2b9u7dq9atW3u7GgAAAADwqWJdGRozZoySk5MVFxenEydOaObMmVqxYoUWL14sh8OhwYMHa9SoUYqMjFR4eLgefPBBtW7dmkxyAAAAAAJOsYKhw4cPa8CAATpw4IAcDoeuuOIKLV68WDfeeKMk6fXXX1eFChXUq1cvt0FXAQAAACDQeD3OkK9lZmbK4XBITmdAPjNkCrld0VbI3ixsfn8qrG4A4GvenjPLqrL8WVFWlde+VJZZtR/TFz0r8XkxM1NyOOR0OhXuh9jA98m6AQAAAKAMKPGgq1blbbTPrwXW4s2vYvSVkrHqL5He8Gdfox97Vl6vmPn7+Cur+8WqaC9cKFD7A1eGAAAAAFgSwRAAAAAASyIYAgAAAGBJBEMAAAAALIlgCAAAAIAlEQwBAAAAsCSCIQAAAACWFLDjDDkdUn5jzPozT3l5HqMkUPO7l2fs8+LzdvwVb/a5t8e/Vdu7rJ43S7O9ymtfKa/bhZIpr+NpBbJAPh8HantzZQgAAACAJREMAQAAALAkgiEAAAAAlkQwBAAAAMCSCIYAAAAAWBLBEAAAAABLCtjU2g6n8s+t7UeBmvYPJefPNJP0F98j3XHZU9B+K800r4W1pz9TqdOXAI6D0lCa+7ywc2pJz7mZkhwlm7VIuDIEAAAAwJIIhgAAAABYEsEQAAAAAEsiGAIAAABgSQRDAAAAACyJYAgAAACAJREMAQAAALCkgB1nCPAVb3Lu+3OMFH+OceKL5ZeWQB6Xwpt9GsjbVZjCtrusjrdTXo8hACgNfjvf+3mgIa4MAQAAALAkgiEAAAAAllSsYCgtLU1XX321qlatqqioKPXs2VPbtm1zK9OxY0fZbDa319ChQ31aaQAAAADwVrGCoZUrV2r48OFavXq1lixZonPnzqlLly46deqUW7n77rtPBw4ccL3Gjx/v00oDAAAAgLeKlUBh0aJFbv9Pnz5dUVFRWr9+vdq3b++aXrlyZcXExPimhgAAAADgB149M+R0OiVJkZGRbtNnzJihGjVqqFmzZhozZox+//13b1YDAAAAAD5X4tTaOTk5GjlypNq2batmzZq5pt95552qW7euYmNjtWnTJj3xxBPatm2b5s6d63E5WVlZysrKcv2fmZlZ0ioBAAAAQJGVOBgaPny4fvjhB61atcpt+v333+/6u3nz5qpVq5Y6deqknTt3Kj4+Ps9y0tLSlJqaWtJqAF6NgVIYf46R4u0YJ1Yd86Y0WXW/sd1A+eXPz1CUTFkd46ys9pUS3SY3YsQILVy4UF988YVq165dYNlWrVpJknbs2OHx/TFjxsjpdLpe+/btK0mVAAAAAKBYinVlyBijBx98UPPmzdOKFStUr169QufZuHGjJKlWrVoe37fb7bLb7cWpBgAAAAB4rVjB0PDhwzVz5kwtWLBAVatW1cGDByVJDodDoaGh2rlzp2bOnKmbbrpJ1atX16ZNm/TII4+offv2uuKKK/yyAQAAAABQEjZjTJHv8LPZPN/EOG3aNA0cOFD79u3TXXfdpR9++EGnTp1SnTp1dOutt+ovf/mLwsPDi7SOzMxMORwOyemUijgPrK283u/sz3uGy+o+AQD4Vnn9DC3LeGboIpmZksMhp9NZ5HiiOIp9m1xB6tSpo5UrV3pVIQAAAAC4FLwaZwgAAAAAyqoSp9YuTd5ePuSSb/niz/Ysy33Nm3WX1Uv0heHYLxluo0FR0VcCT3k9n5dn/vz85hjMiytDAAAAACyJYAgAAACAJREMAQAAALAkgiEAAAAAlkQwBAAAAMCSCIYAAAAAWBLBEAAAAABLKpPjDJEjPfD4cxyDsjpWD+vGpVSaY4kUtG76krVYtb0DeUw6q7aJVdHexceVIQAAAACWRDAEAAAAwJIIhgAAAABYEsEQAAAAAEsiGAIAAABgSQRDAAAAACyJYAgAAACAJQXsOENOhxTup2VbNQe7N+MgFLbP2KeeWXW/lKbSHG8nUNEPy59A7ef+7mv+/BzzBsdYyQRqPy5t9KdLiytDAAAAACyJYAgAAACAJREMAQAAALAkgiEAAAAAlkQwBAAAAMCSCIYAAAAAWBLBEAAAAABLCthxhhxO+W2goYLy2pPb3TPGAih7/Nlm/jxOSrOvcfyXL+V5HLCyXHdvWHW7yyvaE4GAK0MAAAAALIlgCAAAAIAlFSsYSktL09VXX62qVasqKipKPXv21LZt29zKnDlzRsOHD1f16tUVFhamXr166dChQz6tNAAAAAB4q1jB0MqVKzV8+HCtXr1aS5Ys0blz59SlSxedOnXKVeaRRx7RZ599ptmzZ2vlypX69ddfddttt/m84gAAAADgDZsxpsSPrx05ckRRUVFauXKl2rdvL6fTqZo1a2rmzJnq3bu3JGnr1q1q3Lix0tPTde211xa6zMzMTDkcDsnplML9k0HBqgkUSIJw6ZVmfyKBQvGV5+PfispzAgUAsIzMTMnhkNPpVLgfYgOvnhlyOp2SpMjISEnS+vXrde7cOXXu3NlVJiEhQXFxcUpPT/e4jKysLGVmZrq9AAAAAMDfSpxaOycnRyNHjlTbtm3VrFkzSdLBgwcVHBysiIgIt7LR0dE6ePCgx+WkpaUpNTW1pNUoEav+qs2voIHF275SXtu7rNYbngXyOREoK6x6R0ug4qpz+VLiK0PDhw/XDz/8oFmzZnlVgTFjxsjpdLpe+/bt82p5AAAAAFAUJboyNGLECC1cuFBffvmlateu7ZoeExOjs2fPKiMjw+3q0KFDhxQTE+NxWXa7XXa7vSTVAAAAAIASK9aVIWOMRowYoXnz5mn58uWqV6+e2/uJiYmqVKmSli1b5pq2bds27d27V61bt/ZNjQEAAADAB4p1ZWj48OGaOXOmFixYoKpVq7qeA3I4HAoNDZXD4dDgwYM1atQoRUZGKjw8XA8++KBat25dpExyAAAAAHCpFCu1ts3m+YmxadOmaeDAgZL+GHT10Ucf1T//+U9lZWUpKSlJkydPzvc2uYtditTa/sTDwigqfydQAAIB50TAeyRQCCwkULjE/Jxa26txhvyBYKjkOPjKFoIhWAHnRMB7BEOBhWDoEgvkcYYAAAAAoKwq8ThD/uZ0SGXvulDhAvXXAn69vfQK2+7C2oRfpnyPq3W+xz4BvBeo4yNa9fguz9tdmt8H85MpyeHH5XNlCAAAAIAlEQwBAAAAsCSCIQAAAACWRDAEAAAAwJIIhgAAAABYEsEQAAAAAEsiGAIAAABgSQE7zpDDqYAcaMjb/Ov+zN/uTd778pwzv6yiTS499jnKA3+PE8JxUr7QnrhQafaH0hrjiCtDAAAAACyJYAgAAACAJREMAQAAALAkgiEAAAAAlkQwBAAAAMCSCIYAAAAAWFLAptYOVKSgLF8KS+NIewMoazhvAeVXef7ekm/dMyU5/LdergwBAAAAsCSCIQAAAACWRDAEAAAAwJIIhgAAAABYEsEQAAAAAEsiGAIAAABgSQRDAAAAACyJcYZgaWU5H783ChunoDCBvN+82bZA3i4AKA3efl4UhHNu8bHPfI8rQwAAAAAsiWAIAAAAgCUVOxj68ssv1aNHD8XGxspms2n+/Plu7w8cOFA2m83t1bVrV1/VFwAAAAB8otjB0KlTp9SiRQtNmjQp3zJdu3bVgQMHXK9//vOfXlUSAAAAAHyt2AkUkpOTlZycXGAZu92umJiYElcKAAAAAPzNL88MrVixQlFRUWrUqJEeeOABHTt2zB+rAQAAAIAS83lq7a5du+q2225TvXr1tHPnTj311FNKTk5Wenq6goKC8pTPyspSVlaW6//MzExfVwkAAAAA8vB5MNSvXz/X382bN9cVV1yh+Ph4rVixQp06dcpTPi0tTampqXmmOx1SeAnrQA52oGDeHiOM5QOgJPw5Zo2/WfXcZdXthnX4PbV2/fr1VaNGDe3YscPj+2PGjJHT6XS99u3b5+8qAQAAAIDvrwxd7JdfftGxY8dUq1Ytj+/b7XbZ7XZ/VwMAAAAA3BQ7GDp58qTbVZ5du3Zp48aNioyMVGRkpFJTU9WrVy/FxMRo586devzxx9WgQQMlJSX5tOIAAAAA4I1iB0Pr1q3T9ddf7/p/1KhRkqSUlBRNmTJFmzZt0nvvvaeMjAzFxsaqS5cuev7557n6AwAAACCg2IwxAfVoXGZmphwOh5xyKryEKRR42A/wr0BOoBDIdQOsjgQKAIotM1NyOOR0OhUeXtL0avnzewIFAAAAAAhEfk+gUFIOp0qeWxsoA7z9hbQ0f6X057oL2y+Frbug9wtbNr9aozgK6i+0h2fsFwD5ye+cminJ4cf1cmUIAAAAgCURDAEAAACwJIIhAAAAAJZEMAQAAADAkgiGAAAAAFgSwRAAAAAASyIYAgAAAGBJATvOEFDelefxNrwZf8Wf+6U873MUXyCP9eXPMa84Djxjn+NCpTnunFX7S77b7eeBhrgyBAAAAMCSCIYAAAAAWBLBEAAAAABLIhgCAAAAYEkEQwAAAAAsiWAIAAAAgCURDAEAAACwJMYZusTKa976wrarrObML8vtVZp1L0hZ3qcoXwK5PwRy3cor9rm1ePNZRF8pX7gyBAAAAMCSCIYAAAAAWBLBEAAAAABLIhgCAAAAYEkEQwAAAAAsiWAIAAAAgCURDAEAAACwJMYZuoi3Y6AUlnu+vOamL69jGJXl9irNNvGnstwm5RXjdQCBi/O1Z4Fct7KqrI45yZUhAAAAAJZEMAQAAADAkoodDH355Zfq0aOHYmNjZbPZNH/+fLf3jTF65plnVKtWLYWGhqpz587avn27r+oLAAAAAD5R7GDo1KlTatGihSZNmuTx/fHjx+utt97S1KlTtWbNGlWpUkVJSUk6c+aM15UFAAAAAF8pdgKF5ORkJScne3zPGKM33nhDf/nLX3TLLbdIkt5//31FR0dr/vz56tevn3e1BQAAAAAf8ekzQ7t27dLBgwfVuXNn1zSHw6FWrVopPT3d4zxZWVnKzMx0ewEAAACAv/k0tfbBgwclSdHR0W7To6OjXe9dLC0tTampqb6shlcCNe2fldEmgSeQ24Q0z5ce+w0IXKV5fJbVVMuBLJD3aVltz1LPJjdmzBg5nU7Xa9++faVdJQAAAAAW4NNgKCYmRpJ06NAht+mHDh1yvXcxu92u8PBwtxcAAAAA+JtPg6F69eopJiZGy5Ytc03LzMzUmjVr1Lp1a1+uCgAAAAC8Uuxnhk6ePKkdO3a4/t+1a5c2btyoyMhIxcXFaeTIkXrhhRfUsGFD1atXT08//bRiY2PVs2dPX9YbAAAAALxS7GBo3bp1uv76613/jxo1SpKUkpKi6dOn6/HHH9epU6d0//33KyMjQ9ddd50WLVqkkJAQ39UaAAAAALxkM8YEVO6HzMxMORwOyemUeH4IQDGRTQ4AAkMgZz4rqyy5TzMzJYdDTqfTL7kFSj2bHAAAAACUBp+OM4SyG7F782u6vwXqPkNgsmp/8ecx7M0+9fe5paC6eXs+Ls3zYlmtW1n9DIR/0N6e+fP4DdTPgkDGlSEAAAAAlkQwBAAAAMCSCIYAAAAAWBLBEAAAAABLIhgCAAAAYEkEQwAAAAAsiWAIAAAAgCUxzpCPldUc7GW13hI59QEpcPtqadbL23UH6j6VArdupVkvxjhCWeHPvshxUHxcGQIAAABgSQRDAAAAACyJYAgAAACAJREMAQAAALAkgiEAAAAAlkQwBAAAAMCSAja1ttMhhZdw3kBOG1hQysNArncgY78BAPgswIX8OeyGt/zZVzkOio8rQwAAAAAsiWAIAAAAgCURDAEAAACwJIIhAAAAAJZEMAQAAADAkgiGAAAAAFgSwRAAAAAASwrYcYYcTpV8oCEvFJaX3tv87aWZ/92fOffJa4+i8vfYDwX1xUAed6K84txgLVY9xujnJWPV/oLAwpUhAAAAAJZEMAQAAADAknweDD377LOy2Wxur4SEBF+vBgAAAAC84pdnhpo2baqlS5f+byUVA/bRJAAAAAAW5ZcopWLFioqJifHHogEAAADAJ/zyzND27dsVGxur+vXrq3///tq7d68/VgMAAAAAJebzK0OtWrXS9OnT1ahRIx04cECpqalq166dfvjhB1WtWjVP+aysLGVlZbn+z8zM9HWVAAAAACAPmzHGr9nxMzIyVLduXb322msaPHhwnvefffZZpaam5p3R6ZTCS2GgoVLkbb59xjkovkAe4yCQ29Pf43EBgCcFnXs475Q/gdreVv0MLK3vTJnKlEMOOZ1OhfshNvB7au2IiAj96U9/0o4dOzy+P2bMGDmdTtdr3759/q4SAAAAAPg/GDp58qR27typWrVqeXzfbrcrPDzc7QUAAAAA/ubzYGj06NFauXKldu/erW+++Ua33nqrgoKCdMcdd/h6VQAAAABQYj5PoPDLL7/ojjvu0LFjx1SzZk1dd911Wr16tWrWrOnrVQEAAABAifk8GJo1a5avFwkAAAAAPuf3Z4YAAAAAIBD5/MqQrzgdUklTKZTVlIZltd5FUZoprAvar+V5n/sT+w2XCilsfa8spyQur+1dXpXX9g7UevlbqW13piSH/xbPlSEAAAAAlkQwBAAAAMCSCIYAAAAAWBLBEAAAAABLIhgCAAAAYEkEQwAAAAAsiWAIAAAAgCUF7DhDDqdKPtAQAo5Vc/J7w9txRtjnKKpA7muFLbugupfn8XRKc+w2b5TX85JVx8MqjFW326r8dV7y8zBDXBkCAAAAYE0EQwAAAAAsiWAIAAAAgCURDAEAAACwJIIhAAAAAJZEMAQAAADAkgiGAAAAAFhSwI4zBASCsjqWh+Rd3cvr2BCMBeJZWd7uslx3b1h1uwMV7YELBfLYbd7w93eifLfbzwMNcWUIAAAAgCURDAEAAACwJIIhAAAAAJZEMAQAAADAkgiGAAAAAFgSwRAAAAAASyIYAgAAAGBJjDMEFCBQc/0XhTfjAfhzLIHS3KdluT1Lkz/HrCqvYz+V1XoDKH1ldYzDsnre48oQAAAAAEsiGAIAAABgSX4LhiZNmqTLL79cISEhatWqlb799lt/rQoAAAAAis0vwdBHH32kUaNGaezYsfruu+/UokULJSUl6fDhw/5YHQAAAAAUm1+Coddee0333XefBg0apCZNmmjq1KmqXLmy/vGPf/hjdQAAAABQbD7PJnf27FmtX79eY8aMcU2rUKGCOnfurPT09Dzls7KylJWV5frf6XT+8Udmpq+rBlhKwB5BAVsx5MerJitk5kKXTX8BUMZY9rTlrw3//zGBMf5JV+fzYOjo0aPKzs5WdHS02/To6Ght3bo1T/m0tDSlpqbmXVCdOr6uGmApjtKuQH4CtmLIj1dNVsjMhS6b/gKgjLHsacvPG37s2DE5HL5fSamPMzRmzBiNGjXK9X9GRobq1q2rvXv3+mWDgVyZmZmqU6eO9u3bp/Dw8NKuDsox+houFfoaLhX6Gi4Vp9OpuLg4RUZG+mX5Pg+GatSooaCgIB06dMht+qFDhxQTE5OnvN1ul91uzzPd4XBwcOGSCA8Pp6/hkqCv4VKhr+FSoa/hUqlQwT9JsH2+1ODgYCUmJmrZsmWuaTk5OVq2bJlat27t69UBAAAAQIn45Ta5UaNGKSUlRS1bttQ111yjN954Q6dOndKgQYP8sToAAAAAKDa/BEO33367jhw5omeeeUYHDx7Un//8Zy1atChPUgVP7Ha7xo4d6/HWOcCX6Gu4VOhruFToa7hU6Gu4VPzd12zGX3nqAAAAACCA+edJJAAAAAAIcARDAAAAACyJYAgAAACAJREMAQAAALCkgAuGJk2apMsvv1whISFq1aqVvv3229KuEsq4tLQ0XX311apataqioqLUs2dPbdu2za3MmTNnNHz4cFWvXl1hYWHq1atXnoGDgeJ46aWXZLPZNHLkSNc0+hl8af/+/brrrrtUvXp1hYaGqnnz5lq3bp3rfWOMnnnmGdWqVUuhoaHq3Lmztm/fXoo1RlmUnZ2tp59+WvXq1VNoaKji4+P1/PPP68L8W/Q1lMSXX36pHj16KDY2VjabTfPnz3d7vyj96vjx4+rfv7/Cw8MVERGhwYMH6+TJk8WqR0AFQx999JFGjRqlsWPH6rvvvlOLFi2UlJSkw4cPl3bVUIatXLlSw4cP1+rVq7VkyRKdO3dOXbp00alTp1xlHnnkEX322WeaPXu2Vq5cqV9//VW33XZbKdYaZdnatWv1zjvv6IorrnCbTj+Dr/z2229q27atKlWqpM8//1w//vijXn31VVWrVs1VZvz48Xrrrbc0depUrVmzRlWqVFFSUpLOnDlTijVHWfPyyy9rypQpevvtt7Vlyxa9/PLLGj9+vCZOnOgqQ19DSZw6dUotWrTQpEmTPL5flH7Vv39//fe//9WSJUu0cOFCffnll7r//vuLVxETQK655hozfPhw1//Z2dkmNjbWpKWllWKtUN4cPnzYSDIrV640xhiTkZFhKlWqZGbPnu0qs2XLFiPJpKenl1Y1UUadOHHCNGzY0CxZssR06NDBPPzww8YY+hl864knnjDXXXddvu/n5OSYmJgYM2HCBNe0jIwMY7fbzT//+c9LUUWUE926dTP33HOP27TbbrvN9O/f3xhDX4NvSDLz5s1z/V+UfvXjjz8aSWbt2rWuMp9//rmx2Wxm//79RV53wFwZOnv2rNavX6/OnTu7plWoUEGdO3dWenp6KdYM5Y3T6ZQkRUZGSpLWr1+vc+fOufW9hIQExcXF0fdQbMOHD1e3bt3c+pNEP4Nvffrpp2rZsqX69OmjqKgoXXnllXr33Xdd7+/atUsHDx50628Oh0OtWrWiv6FY2rRpo2XLlumnn36SJH3//fdatWqVkpOTJdHX4B9F6Vfp6emKiIhQy5YtXWU6d+6sChUqaM2aNUVeV0XfVds7R48eVXZ2tqKjo92mR0dHa+vWraVUK5Q3OTk5GjlypNq2batmzZpJkg4ePKjg4GBFRES4lY2OjtbBgwdLoZYoq2bNmqXvvvtOa9euzfMe/Qy+9PPPP2vKlCkaNWqUnnrqKa1du1YPPfSQgoODlZKS4upTnj5T6W8ojieffFKZmZlKSEhQUFCQsrOzNW7cOPXv31+S6Gvwi6L0q4MHDyoqKsrt/YoVKyoyMrJYfS9ggiHgUhg+fLh++OEHrVq1qrSrgnJm3759evjhh7VkyRKFhISUdnVQzuXk5Khly5Z68cUXJUlXXnmlfvjhB02dOlUpKSmlXDuUJx9//LFmzJihmTNnqmnTptq4caNGjhyp2NhY+hrKhYC5Ta5GjRoKCgrKk1np0KFDiomJKaVaoTwZMWKEFi5cqC+++EK1a9d2TY+JidHZs2eVkZHhVp6+h+JYv369Dh8+rKuuukoVK1ZUxYoVtXLlSr311luqWLGioqOj6WfwmVq1aqlJkyZu0xo3bqy9e/dKkqtP8ZkKbz322GN68skn1a9fPzVv3lx33323HnnkEaWlpUmir8E/itKvYmJi8iRZO3/+vI4fP16svhcwwVBwcLASExO1bNky17ScnBwtW7ZMrVu3LsWaoawzxmjEiBGaN2+eli9frnr16rm9n5iYqEqVKrn1vW3btmnv3r30PRRZp06dtHnzZm3cuNH1atmypfr37+/6m34GX2nbtm2eIQJ++ukn1a1bV5JUr149xcTEuPW3zMxMrVmzhv6GYvn9999VoYL718WgoCDl5ORIoq/BP4rSr1q3bq2MjAytX7/eVWb58uXKyclRq1atir4yr9M/+NCsWbOM3W4306dPNz/++KO5//77TUREhDl48GBpVw1l2AMPPGAcDodZsWKFOXDggOv1+++/u8oMHTrUxMXFmeXLl5t169aZ1q1bm9atW5dirVEeXJhNzhj6GXzn22+/NRUrVjTjxo0z27dvNzNmzDCVK1c2H374oavMSy+9ZCIiIsyCBQvMpk2bzC233GLq1atnTp8+XYo1R1mTkpJiLrvsMrNw4UKza9cuM3fuXFOjRg3z+OOPu8rQ11ASJ06cMBs2bDAbNmwwksxrr71mNmzYYPbs2WOMKVq/6tq1q7nyyivNmjVrzKpVq0zDhg3NHXfcUax6BFQwZIwxEydONHFxcSY4ONhcc801ZvXq1aVdJZRxkjy+pk2b5ipz+vRpM2zYMFOtWjVTuXJlc+utt5oDBw6UXqVRLlwcDNHP4EufffaZadasmbHb7SYhIcH89a9/dXs/JyfHPP300yY6OtrY7XbTqVMns23btlKqLcqqzMxM8/DDD5u4uDgTEhJi6tevb/7v//7PZGVlucrQ11ASX3zxhcfvZykpKcaYovWrY8eOmTvuuMOEhYWZ8PBwM2jQIHPixIli1cNmzAVDCAMAAACARQTMM0MAAAAAcCkRDAEAAACwJIIhAAAAAJZEMAQAAADAkgiGAAAAAFgSwRAAAAAASyIYAgAAAGBJBEMAAAAALIlgCAAAAIAlEQwBAAAAsCSCIQAAAACWRDAEAAAAwJL+H+66wAQRIA+YAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(16, 30, 100)\n",
    "mask = create_subsequence_mask(t, r=.15) # default settings\n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'sample 0 subsequence mask (sync=False) - default mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[1], cmap='cool')\n",
    "plt.title(f'sample 1 subsequence mask (sync=False) - default mean: {mask[1].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(16, 30, 100)\n",
    "mask = create_subsequence_mask(t, r=.5) # 50% of values masked\n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'sample 0 subsequence mask (r=.5) mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(16, 30, 100)\n",
    "mask = create_subsequence_mask(t, lm=5) # average length of mask = 5 \n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'sample 0 subsequence mask (lm=5) mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(16, 30, 100)\n",
    "mask = create_subsequence_mask(t, stateful=False) # individual time steps masked \n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'per sample subsequence mask (stateful=False) mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(1, 30, 100)\n",
    "mask = create_subsequence_mask(t, sync=True) # all time steps masked simultaneously\n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'per sample subsequence mask (sync=True) mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(1, 30, 100)\n",
    "mask = create_variable_mask(t) # masked variables\n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'per sample variable mask mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(1, 30, 100)\n",
    "mask = create_future_mask(t, r=.15, sync=True) # masked steps\n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'future mask mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = torch.rand(1, 30, 100)\n",
    "mask = create_future_mask(t, r=.15, sync=False) # masked steps\n",
    "mask = create_future_mask(t, r=.15, sync=True) # masked steps\n",
    "test_eq(mask.dtype, torch.bool)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.pcolormesh(mask[0], cmap='cool')\n",
    "plt.title(f'future mask mean: {mask[0].float().mean().item():.3f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "def create_mask(o,  r=.15, lm=3, stateful=True, sync=False, subsequence_mask=True, variable_mask=False, future_mask=False):\n",
    "    if r <= 0 or r >=1: return torch.zeros_like(o).bool()\n",
    "    if int(r * o.shape[1]) == 0:\n",
    "        variable_mask = False \n",
    "    if subsequence_mask and variable_mask:\n",
    "        random_thr = 1/3 if sync == 'random' else 1/2\n",
    "        if random.random() > random_thr: \n",
    "            variable_mask = False\n",
    "        else:\n",
    "            subsequence_mask = False \n",
    "    elif future_mask:\n",
    "        return create_future_mask(o, r=r)\n",
    "    elif subsequence_mask:\n",
    "        return create_subsequence_mask(o, r=r, lm=lm, stateful=stateful, sync=sync)\n",
    "    elif variable_mask:\n",
    "        return create_variable_mask(o, r=r)\n",
    "    else:\n",
    "        raise ValueError('You need to set subsequence_mask, variable_mask or future_mask to True or pass a custom mask.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "\n",
    "class MVP(Callback):\n",
    "    order = 60\n",
    "\n",
    "    def __init__(self, r: float = .15, subsequence_mask: bool = True, lm: float = 3., stateful: bool = True, sync: bool = False, variable_mask: bool = False,\n",
    "                 future_mask: bool = False, custom_mask: Optional = None, sel_vars: Optional[list] = None, nan_to_num: int = 0, \n",
    "                 window_size: Optional[tuple] = None, dropout: float = .1, crit: callable = None, weights_path: Optional[str] = None, \n",
    "                 target_dir: str = './models/MVP', fname: str = 'model', save_best: bool = True,\n",
    "                 verbose: bool = False):\n",
    "        r\"\"\"\n",
    "        Callback used to perform the pretext task of reconstruct the original data after a binary mask has been applied.\n",
    "\n",
    "        Args:\n",
    "            r:                proba of masking.\n",
    "            subsequence_mask: apply a mask to random subsequences.\n",
    "            lm:               average mask len when using stateful (geometric) masking.\n",
    "            stateful:         geometric distribution is applied so that average mask length is lm.\n",
    "            sync:             all variables have the same masking.\n",
    "            variable_mask:    apply a mask to random variables. Only applicable to multivariate time series.\n",
    "            future_mask:      used to train a forecasting model.\n",
    "            custom_mask:      allows to pass any type of mask with input tensor and output tensor. Values to mask should be set to True.\n",
    "            sel_vars:         allows to pass a list of variables to mask. If None, all variables will be masked.\n",
    "            nan_to_num:       integer used to fill masked values\n",
    "            window_size:      allows you to pass a fixed window size or tuple of window sizes to train MVP with on sequences of different length. \n",
    "                              You may pass int(s) or float(s).\n",
    "            dropout:          dropout applied to the head of the model during pretraining.\n",
    "            crit:             loss function that will be used. If None MSELossFlat().\n",
    "            weights_path:     indicates the path to pretrained weights. This is useful when you want to continue training from a checkpoint. It will load the\n",
    "                              pretrained weights to the model with the MVP head.\n",
    "            target_dir :      directory where trained model will be stored.\n",
    "            fname :           file name that will be used to save the pretrained model.\n",
    "            save_best:        saves best model weights\n",
    "    \"\"\"\n",
    "        assert subsequence_mask or variable_mask or future_mask or custom_mask, \\\n",
    "            'you must set (subsequence_mask and/or variable_mask) or future_mask to True or use a custom_mask'\n",
    "        if custom_mask is not None and (future_mask or subsequence_mask or variable_mask):\n",
    "            warnings.warn(\"Only custom_mask will be used\")\n",
    "        elif future_mask and (subsequence_mask or variable_mask):\n",
    "            warnings.warn(\"Only future_mask will be used\")\n",
    "        store_attr(\"subsequence_mask,variable_mask,future_mask,custom_mask,dropout,r,lm,stateful,sync,crit,weights_path,fname,save_best,verbose,nan_to_num\")\n",
    "        self.PATH = Path(f'{target_dir}/{self.fname}')\n",
    "        if not os.path.exists(self.PATH.parent):\n",
    "            os.makedirs(self.PATH.parent)\n",
    "        self.path_text = f\"pretrained weights_path='{self.PATH}.pth'\"\n",
    "        self.window_size = window_size\n",
    "        self.sel_vars = sel_vars\n",
    "\n",
    "    def before_fit(self):\n",
    "        self.run = not hasattr(self, \"gather_preds\")\n",
    "        if 'SaveModelCallback' in [cb.__class__.__name__ for cb in self.learn.cbs]:\n",
    "            self.save_best =  False # avoid saving if SaveModelCallback is being used\n",
    "        if not(self.run): return\n",
    "\n",
    "        # prepare to save best model\n",
    "        self.best = float('inf')\n",
    "\n",
    "        # modify loss for denoising task\n",
    "        self.old_loss_func = self.learn.loss_func\n",
    "        self.learn.loss_func = self._loss\n",
    "        if self.crit is None: \n",
    "            self.crit = MSELossFlat()\n",
    "        self.learn.MVP = self\n",
    "        self.learn.TSBERT = self\n",
    "\n",
    "        # remove and store metrics\n",
    "        self.learn.metrics = L([])\n",
    "        \n",
    "        device = self.learn.dls.device\n",
    "        \n",
    "        if self.sel_vars is not None:\n",
    "            self.sel_vars = torch.Tensor(listify(self.sel_vars)).long().to(device=device)\n",
    "\n",
    "        # change head with conv layer (equivalent to linear layer applied to dim=1)\n",
    "        assert hasattr(self.learn.model, \"head\"), \"model must have a head attribute to be trained with MVP\"\n",
    "        self.learn.model.head = nn.Sequential(nn.Dropout(self.dropout),\n",
    "                                              nn.Conv1d(self.learn.model.head_nf, self.learn.dls.vars, 1)\n",
    "                                             ).to(device=device)\n",
    "        if self.weights_path is not None:\n",
    "            transfer_weights(self.learn.model, self.weights_path, device=device, exclude_head=False)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            xb = torch.zeros(2, self.learn.dls.vars, self.learn.dls.len).to(device=device)\n",
    "            assert xb.shape == self.learn.model(xb).shape, f'the model cannot reproduce the input shape {xb.shape}, {self.learn.model(xb).shape}'\n",
    "            \n",
    "        if self.window_size:\n",
    "            if isinstance(self.window_size, float) or self.window_size == 1: \n",
    "                self.window_size = int(round(self.window_size * self.learn.dls.len))\n",
    "            elif is_listy(self.window_size): \n",
    "                self.window_size = list(self.window_size)\n",
    "                for i in range(len(self.window_size)):\n",
    "                    if isinstance(self.window_size[i], float) or self.window_size[i] == 1: \n",
    "                        self.window_size[i] = int(round(self.window_size[i] * self.learn.dls.len))\n",
    "        \n",
    "    def before_batch(self):\n",
    "        original_mask = torch.isnan(self.x)\n",
    "        if self.custom_mask is not None:\n",
    "            new_mask = self.custom_mask(self.x)\n",
    "        else:\n",
    "            new_mask = create_mask(self.x, r=self.r, lm=self.lm, stateful=self.stateful, sync=self.sync, subsequence_mask=self.subsequence_mask,\n",
    "                                   variable_mask=self.variable_mask, future_mask=self.future_mask).bool()\n",
    "        if original_mask.any(): \n",
    "            self.mask = torch.logical_and(new_mask, ~original_mask)\n",
    "        else: \n",
    "            self.mask = new_mask\n",
    "        \n",
    "        if self.sel_vars is not None:\n",
    "            self.mask[:, ~self.sel_vars] = 0\n",
    "            \n",
    "#         self.learn.yb = (torch.nan_to_num(self.x, self.nan_to_num),) # Only available in Pytorch 1.8\n",
    "        self.learn.yb = (torch_nan_to_num(self.x, self.nan_to_num),)\n",
    "        self.learn.xb = (self.yb[0].masked_fill(self.mask, self.nan_to_num), )\n",
    "        if self.window_size:\n",
    "            if is_listy(self.window_size): ws = np.random.randint(*self.window_size)\n",
    "            else: ws = self.window_size\n",
    "            w_start = np.random.randint(0, self.x.shape[-1] - ws)\n",
    "            self.learn.xb = (self.learn.xb[0][..., w_start:w_start+ws], )\n",
    "            self.learn.yb = (self.learn.yb[0][..., w_start:w_start+ws], )\n",
    "            self.mask = self.mask[..., w_start:w_start+ws]\n",
    "\n",
    "    def after_epoch(self):\n",
    "        val = self.learn.recorder.values[-1][-1]\n",
    "        if self.save_best:\n",
    "            if np.less(val, self.best):\n",
    "                self.best = val\n",
    "                self.best_epoch = self.epoch\n",
    "                torch.save(self.learn.model.state_dict(), f'{self.PATH}.pth')\n",
    "                pv(f\"best epoch: {self.best_epoch:3}  val_loss: {self.best:8.6f} - {self.path_text}\", self.verbose or (self.epoch == self.n_epoch - 1))\n",
    "            elif self.epoch == self.n_epoch - 1:\n",
    "                print(f\"\\nepochs: {self.n_epoch} best epoch: {self.best_epoch:3}  val_loss: {self.best:8.6f} - {self.path_text}\\n\")\n",
    "\n",
    "    def after_fit(self):\n",
    "        self.run = True\n",
    "\n",
    "    def _loss(self, preds, target):\n",
    "        return self.crit(preds[self.mask], target[self.mask])\n",
    "\n",
    "    def show_preds(self, max_n=9, nrows=3, ncols=3, figsize=None, sharex=True, **kwargs):\n",
    "        b = self.learn.dls.valid.one_batch()\n",
    "        self.learn._split(b)\n",
    "        self.learn('before_batch')\n",
    "        xb = self.xb[0].detach().cpu().numpy()\n",
    "        bs, nvars, seq_len = xb.shape\n",
    "        masked_pred = torch.where(self.mask, self.learn.model(*self.learn.xb), tensor([np.nan], device=self.learn.x.device))\n",
    "        masked_pred = masked_pred.detach().cpu().numpy()\n",
    "        ncols = min(ncols, math.ceil(bs / ncols))\n",
    "        nrows = min(nrows, math.ceil(bs / ncols))\n",
    "        max_n = min(max_n, bs, nrows*ncols)\n",
    "        if figsize is None:\n",
    "            figsize = (ncols*6, math.ceil(max_n/ncols)*4)\n",
    "        fig, ax = plt.subplots(nrows=nrows, ncols=ncols,\n",
    "                               figsize=figsize, sharex=sharex, **kwargs)\n",
    "        idxs = np.random.permutation(np.arange(bs))\n",
    "        colors = list(mcolors.TABLEAU_COLORS.keys()) + \\\n",
    "            random_shuffle(list(mcolors.CSS4_COLORS.keys()))\n",
    "        i = 0\n",
    "        for row in ax:\n",
    "            for col in row:\n",
    "                color_iter = iter(colors)\n",
    "                for j in range(nvars):\n",
    "                    try:\n",
    "                        color = next(color_iter)\n",
    "                    except:\n",
    "                        color_iter = iter(colors)\n",
    "                        color = next(color_iter)\n",
    "                    col.plot(xb[idxs[i]][j], alpha=.5, color=color)\n",
    "                    col.plot(masked_pred[idxs[i]][j],\n",
    "                             marker='o', markersize=4, linestyle='None', color=color)\n",
    "                i += 1\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "TSBERT = MVP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsai.data.external import get_UCR_data, check_data\n",
    "from tsai.data.preprocessing import TSStandardize, TSNan2Value\n",
    "from tsai.data.core import TSCategorize, get_ts_dls\n",
    "from tsai.learner import ts_learner\n",
    "from tsai.models.InceptionTimePlus import InceptionTimePlus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X      - shape: [1272 samples x 1 features x 84 timesteps]  type: memmap  dtype:float32  isnan: 0\n",
      "y      - shape: (1272,)  type: memmap  dtype:<U1  n_classes: 2 (636 samples per class) ['1', '2']  isnan: False\n",
      "splits - n_splits: 2 shape: [20, 1252]  overlap: False\n"
     ]
    }
   ],
   "source": [
    "dsid = 'MoteStrain'\n",
    "X, y, splits = get_UCR_data(dsid, split_data=False)\n",
    "check_data(X, y, splits, False)\n",
    "X[X<-1] = np.nan # This is to test the model works well even if nan values are passed through the dataloaders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.270972</td>\n",
       "      <td>1.194974</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-train\n",
    "tfms  = [None, [TSCategorize()]]\n",
    "batch_tfms = [TSStandardize(by_var=True)]\n",
    "unlabeled_dls = get_ts_dls(X, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "learn = ts_learner(unlabeled_dls, InceptionTimePlus, cbs=[MVP(fname=f'{dsid}', window_size=(.5, 1))]) # trained on variable window size\n",
    "learn.fit_one_cycle(1, 3e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.837741</td>\n",
       "      <td>1.200484</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = ts_learner(unlabeled_dls, InceptionTimePlus, cbs=[MVP(weights_path=f'models/MVP/{dsid}.pth')])\n",
    "learn.fit_one_cycle(1, 3e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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v6/pq0uKrISLqTyz8ETlISdPxtQ+X8WAnB59XxueemIJs09NhXg87/oiIiIiIiIiIahUrhT+fC2M+BRH3eWstDV3nvhERUae59x2GyAJ5VUMyr7b0uZpu4OuXVzG/mYXikfALT89gYiDQ4SvsHLPjjzP+iIiIiIiIiIgAVA9IKx57HuTuhSMjYQQUD9KFEpbiOasvh4io77DwR9QjhmHgT957gK/8aL7pRY2uG/jmlVXcWU/DK0v4uSdnMDMY7NKVdoYil19eVJ7cIiIisp2dTBHfvrqGdKFk9aUQERERuYpaqkR9et27LeuRJZwYF3GfKYuvhoio/7j3HYaoxzZSBawnC9AqRbxiqbFOOMMw8Or1dVxfTUGWJPzM+SkcGgl1+WrbZ0Z9NvhzEhERUe/84NYGLi0lcHkpYfWlEBEREbmKOeNPdve27JlK3Oft9TQ0HhonIuood7/DEPXQzbW0+f/Hsyp+eGvjwM8xDAPfu7mBy0sJSBLwuScmzQHIdicKfyWdhT8iIiI7KZQ0LGxlAZRjyImIiIiod8SMP8Xr3qhPAJgZDCLi9yKvapjfylh9OUREfYWFP6IeMAwDN9fK0QXnZ2MAgA8fJHBvs/7CxjAMfP/mBi7ejwMAfuqxSZyaiHb9WjvFjPrUeGqLiIjITu5tZsxT1QV25hMRERH1VHXGn7u3ZWVZwsmJ8uH2G4z7JCLqKHe/wxD1yHqqgEROheKR8NGTY3jq0CAA4NtX15ArPnrS3jAMfOf6Ot6vFP0+dXYcj00P9PCK2yey6kuaAcNg8Y+IiMgubtWkEDQaPU5EREREnWFGfbq88AcAZybLe113N9JclxIRdRDfYYh6QHT7HR2NwOeV8RMnRjEc9iFdKOG7N9Yf+lhdN/DXV9bw4YNyvOdPPjaB87ODFlx1e7xyObJCNwwwqp2IiMgeiiUdCzVRStxgISIiImrf7fU0/vDt+9jOFA/8WLWy/vKx8IeJAT8GQwpUzcDdzfTBn0BERA3hOwxRl5VjPsuLl1OVCAPFI+Mzj09CliTcWE2ZkQaabuDrl1dwbSUJWZLwuXNTODcTs+za21F7ck2cZiMiIiJrLWxlHorhLvI9moiIiKgt6UIJf31lFcvxPG6tHRxZWWTHn0mSJJyujLVh3CcRUefwHYaoy9aSBSRzKnxeGUdGw+afT8YCeP7oMADgO9fXEc8W8bUPl3FrLQ2PLOFnzk/h9KRzZvrt5pElyFK564+FPyIiInu4tV4+jDQVCwBgxx8RERFRu757fd1cUzVyqKo640/q6nU5hdj7WtjKIq8+Og6HiIiax8IfUZdVYz7Dj5zmev7oMCYGAsirGv7dGwu4u5GB4pHw809N48R4xIrL7ShvZRFb0pj1SUREZLWSpuPeZjnmU8wOLpS4uUJERETUqtvrKdxer0ZUFtSDC38l0fHn5bYsAIxE/BiL+qHpxkOzqImIqHV8hyHqonLMZ7nwJ2I+a3lkCZ95fAJeWYKqGfB5ZfzC0zM4PBJ+5GOdSOTVqzq7CYiIiKy2sJ1FsaQjGvDi8HB5rcGOPyIiIqLW5FUN372+AQCIBrwAGuv4Ex/DGX9Vouvv+mrS4ishIuoPfIch6qLVZB6pfKkc81mnmDcS8eMz5yYxNxzCL16YwexQqMdX2T2i409lxx8REZHlxAnq4+MR+JXK4RzNgKbzfZqIiIioWa/d2kS6UMJQSDFHuTSSplCN+uS2rHCqMudvKZ5DIqtafDVERM7HdxiiLhKDiY+PheHdZ0F3aiKKLzwzi6lYsFeX1hPiZy5xxh8REZGlNN3A3c1y4e/keOShjSbO4iUiIiJqzuJ2FpeWEgCAT52dQMjnAdBYmoJYe3HGX1UsqODwSAiGAbw1v2315RAROR4Lf0QHMAwDa8l806fhDcMwc95PVk4uuY0is+OPiIjIDha3syioOsJ+D6ZjQXhkydxsamQWDRERERGVlTQdr15bAwA8MRPD3HAIfm+58FdoqvDHbdlaLxwbAQBcXU4ikWPXHxFRO/gOQ3SA1+9s4T+8eR8XF3ea+rzlRDXm8/Bw/8R3NkMsYkuc8UdERGSpW5XDSMfHIpArB3N83vL7dEE7OJKKiIiIiMrevLeNnayKiN+Lnzg5CqC6rmqk4098jPgcKpsZDOLQcAi6YeDte+z6IyJqB99hiPaRyKp4d6Fc8NvONHfa6OaaiPmM7Bvz2c/EjL8SO/6IiIgso+sG7myImM9qCoHP0/gGFREREREBG6kC3pkv7xN94swYAkq5088vDlQ11PFX3iPxyoz63O2FY+VZiVdX2PVHRNQOd1YjiBr02u1NM+Kzmfk3hmHg9lp5g+3URKQr1+YEouOvyNlBREREllmK55Aragj6PJgdqs4T9iuNz6IhIiIiIuB7N9ahGwZOjEdwouZAlYj6LJZ06AeMijGjPtnx94jZoRDmhkPQdAPvcNYfEVHL+A5DVMdyPGd27QHNFf6W4jmkCyX4FRmHR8LduDxHEKfX2PFHRERknVvr1RQCueZkuY8HdIiIiIgatpMp4sFODpIEfPz02EN/Vxvbud/aStMN84C5z6XpUAd5sdL1d2U5iWSeXX9ERK3gOwzRHgzDwA9ubgAAIn4vgOZOw99aq87R8bg4usGc8ccNRSIiIkvouoHblfl+J8YfTiEwZ/ypfJ8mIiIiOsi1lSQA4MhIGAMB5aG/88gSlMq4k/3WVrWHyhUW/vY0OxTC7FCQXX9ERG3gOwz1rZ1MEelCqaXPvbmWxkoiD59XxkvHRwAApQOiGgRdN8yT9acmogd8dH8Ti1i1wd8dERERddZyIodMQYNfkXFoOPTQ34nCHzv+iIiIiPZnGAauVgp/Z6cG9vwY81CVptX9OmLd5ZElVx8UP8iLx8p7cZeX2PVHRNQKFv6oL+1kivh3byzgKz+6h1s1cZ2NKGk6Xru9CQB45vAQYsHyKa5Goz4zxRIyBQ0eWXpkg81tvB4R9ckNRSIiIiuIbr9jo4+mEJiFP874IyIiItrXg50cUvnySJfjY3uPdBFz/vbt+Kusu9jtt7+5YXb9ERG1g+8y1Jfeu7+Dkm5A1Qx87cMVvHl3C4bRWNfZ+4txJHMqogEvnjk81PSmmPg4n1d2/ektEXPRzHxEIiIi6ox0oYQry+WT6ScnIo/8vZ+FPyIiIqKGiDXV6YkovHWKdo2kKahaeW9K7JdQfbVdfyl2/RERNYWFP+o72WIJVysLsmOVU1iv39nCX19ZPbDzLFss4a175ZNELx8fheKRq3GVWmOFw+oijk8vr9zc746IiIg654c3N1As6ZiKBXBs9NGT6aLwV2Dhj4iIiKiuQknD7cpIl3oxn0DN2qqBGX+iSEj1zQ4FMWN2/e1YfTlERI7CdxnqOxcX4yjpBiZjAfzck9P41NlxyJKEaysp/Ml7D5At1p/798bdLRRLOsYH/Dg7VZ7P522ya81cxPH0VjXqU+eGIhERUS8tbmdxfTUFSQI+eWYckvTousTnqcRRlerPoSEiIiJyu9vraaiagaGQgqlYoO7HNdbxx6jPRkmShJcqXX+XlhLs+iMiagLfZaivqJqODx8kAJTn80mShPOzg/gbT8/Ar8hYjufxH99axFI8h+1MEZvpAtaTeawkcri9nsalB+VOwY+dHDM3yHyVxZimG9D0gzvXilzEmXxNdksSERFR+zTdwHeurwMAnpwdxPjA3htUnPFHREREdDCRKnV2amDPw1RCdcZf/UNVTIlqTm3X33eurzc8xoeIyO28Vl8AUSddXU4iV9QQCyo4MVadZXNoJIRfee4QvnpxCfGsiv/09mLdr3F8PIK54ZD537WLMVXT4ZE9+14DT29Vidz7Egt/REREPfPe/R1sZ4oI+Tx46fhI3Y9r5FQ6ERERkZslsioe7OQgScDZ6foxn0CzHX9MiWqEJEn45Jlx/Ic37+PuRgZXlpM4NxOz+rKIiGyPlQnqG7pu4L375czvpw8NQpYfXkQNh334W88fwvHxCHxeGX5FRsjnQcTvxUBQwVBIwfRgAB8/NfbQ53lkCZ7K12pkY0wtVU5vMa8dXrm5mFQiIiJqTzKv4s27WwCAj54cQ0Cpf2DJz44/IiIion1dWy13+80NhTAQUPb92EZm/BXN8TDcM2rUaMSPlyuH2b5/cwOJHCM/iYgOwo4/6ht3N9OIZ1UEFA8en9779E9A8eDnnpxu+msrHhmarkFtYGOsqJUjHTjjr9r1yMIfERFRb3z/xgZUzcDMUNCcV1wPoz6JiIiI6jMMA9dWqjGfB2mo46/ElKhWXDg0hLsbGSzFc/jW1TX80oWZfWNXiYjcju8y1DfeXSh3+z05GzMXW50iIhhKjcz4q3T8dfoanKiZ3xsRERG1Z34zg9vraciShE+cHj9wM0SsVQos/BERERE9YjmRRzyrwueVcWI8cuDHmx1/pYNn/Hl5WLwpsizhpx6fgOKRsLidxcXFuNWXRERka6xMUF9YjuewHM/DI0t4cm6w41+/mRPxnPFXVZ3xxw1FIiKibippOr57Yx0A8NShQYxF/Qd+joiY0nSD79VEREREu1xdLnf7nayMjDmI31uOWN8v6lNl1GfLBkM+fPRkeTzPa7c2sZ0pWnxFRET2xXcZ6gui2+/s1ADC/s4n2DYTWcnCX5Xo+FM1A4bBrj8iIqJueWdhB/GsiojfixePDTf0Of6aDaxG5hgTERERuYWq6bi5lgLQWMwnUDM/eZ91lfg7hSlRLTk/G8PhkRBKuoFvXlmFzoQpIqI98V2GHG8nU8SdjTQA4MKhwa58j2rh7+AFBQt/VV65+jto5HdHREREzVM1He/MbwMAPnZqzDxtfhBJkjjnj4iIiGgPdzbSKJZ0DAQVzA4FG/ocM+qzgY4/7hm1RpIk/ORjE/ArMlYSebxTaQQgIqKH8V2GHO+9+zswDODYWBgjkYNjrVpR7Vw7eFOsWClwMbah+nsDgJLODUUiIqJuWInnoWoGogEvTk0cPH+mlp+FPyIiIqJHiJjPx6YGDpybLPga6PirFv44469V0YCCV06NAwDeuLuF9VTe4isiIrIfVibI0bLFkrkYu3BoqGvfR5zEaiQGSy2J2AYu4iRJeijuk4iIiDrv/nYWADA3HGp4Y0oQG1QFFv6IiIiIAAC5omaurx5rMOYTqM74K5b0uhGUaomHxTvh7FQUJ8Yj0HQDP7q9afXlEBHZDt9lyNEuLsZR0g1MDAQajl5ohRn12cCmWJGxDQ/xVn4PJc4OIiIi6gqxMXVoONT054pNJxb+iIiIiMoWtjMwDGA06kcspDT8eb4G5idzz6gzJEnCx06OQZYkzG9msZZk1x8RUS2+y5BjFUoaLi7GAQDPHRlq+oR7M5rpWhOxDTy9VeaV2fFHRETULXlVM+ON5loo/PkVRn0SERER1VrYKh+qOjLS3NrKI1dTj+rN+ROHohUv94zaFQspOD0ZBQC8eW/b4qshIrKXrr7L/OAHP8DnP/95TE9PQ5Ik/Nmf/Vk3vx25zKUHCRRUHcNhH06MNzfPplmiiKc2MKdObJz5uIgDUNMtyY4/IiKijlvczsIwgJGIDxG/t+nP93kqkVR8nyYiIiKCYRhY2MoAAA4Ph5v+fDNGXdP2/HtxKJoz/jrj+aPDkCTgznoam+mC1ZdDRGQbXa1MZDIZPPnkk/i93/u9bn4bciFV0/He/R0AwLNd7vYDqiexGon6rC7iWPgDAG9lMVuqk29PREREraud79cKsTnFjj8iIiIiYCNdQKagQfFImB4MNP35Ys5fvY6/IlOiOmo47MPJ8XLX39vs+iMiMjV/LLgJn/vc5/C5z32u4Y8vFAooFKqnM5LJZDcui/rA1eUkMgUN0YAXZyYbH7TcqmrXWuNRnzy9VaZwxh8RUcdwrUS7LbYx3w+oOZVe2vtUOhERkVNwnUSdcH+reqjK20JxzjxUtcceiGEYNXtGLPx1ynNHh3BzLYUbaym8eGwEQ2Gf1ZdERGQ5W73L/O7v/i5isZj5f3Nzc1ZfEtmQpht4Z0F0+w3DI3e/wFad8bd/8UrTDWg6O/5qNTMfkYiI9se1EtVK5FTsZFXIkoSZwWBLX0OcNmfHX333t7J4d2Eb793fwcXFOD5YjOPSgwQuLyUwv5mB3uNUA8MwcGsthbfubZvrTiIi4jqJOmN+q71DVX5xqGqPjr+SbsCovHVzz6hzxqMBHBsLwzCAt+fZ9UdEBHS5469Zv/mbv4kvf/nL5n8nk0ku1OgRN1ZTSOZUhHwePD7d/W4/oLogO2j+TW1hkLENZV6ZM/6IiDqFayWqJbr9JmN+BBRPS1/Dr7Dwt59cUcOfvr8E3ahfYIsGvDg3E8O5mVhLcxabUShp+M61dVxfTQEAtjMFfObxya7H3hMROQHXSdSuYknHcjwHADgy0vx8P2D/jr/afRGmRHXW80eHcXcjg2srKbx4fAQDAcXqSyIispStCn9+vx9+v9/qyyAbMwzDPL1z4fBQz05IVaM+998UK1Q2zbyyBLkHnYhOoJgz/rihSETULq6VqJYo/M0NtXYiHajp+OvCAZ28qiGeVTEx4HdsYSpVUKEbBhSPhONjEegGoBsGdKN8Yn81mUcqX8KP72zhzbvbODEewfnZGGaHgh3/mVcTefzV5RXEK12eAHBtJYWw34uPnhxr+esalaKmU/+NiIgErpOoXQ92stB0AwNBBYOh1gpH1Rl/j8aoqyWRECXxfbfDpmJBHBoO4f52Fu/O7+ATZ8atviQiIkvZqvBHdJA7G2lsZ4rwKzLOz8Z69n3NuMoDTsObWe1edvsJzcxHJCIiosYYhoH729UZNK0y46i60PH36rV13FxL4W8+N9dyFKnVcsXypl0s5MPnnph65O9Lmo5b62l8+CCO5XgeN9dSuLmWwnDYh3MzMZydiiLka++WyzAMvHd/B6/d2oJulDcjP3duEjvZIr55ZQ3vzO8g4vfi6UNDDX/NkqZjfiuD66sp3NvI4MhoGD97foqbkERE5GoLlZjPIyOhlt8T9+v4K3K+X1c9f3QY97ezuLyUwPNHhxHuchIDEZGd8RWQHMMwDLx1rzzb76nZQfMUVS/4GixeicIfYz6rxDDsEgt/REREHbOZLiJb1KB4JEzFAi1/HV8XC39bmQIAYDtddGzhL1sp/IXqRKl6PTLOTg3g7NQA1lN5XHqQwPXVFLYzRfzg5gZ+dHsTx8ciODczgEPDzW8iZgolfPPqKuY3yxuRJyci+PTZCQQUD6YHg8gUNPzo9ia+f3MDEb8XJyeidb+WYRh4sJPD9dUUbq2nHpo9dHs9jWsrKTzWoxh9IiIiO1rYygAADo904FDVHjP+VBb+ump2KIjpwQCW43m8u7CDj51qPRGBiMjpulr4S6fTuH37tvnf9+7dw8WLFzE8PIxDhw5181tTH7q/ncVaMg/FIzV1orkTGp7xJ2Ib2PFnUiqRp5zxR0RE1Dmi229mKGgesmmFeSq9C4U/UTTLFEsd/9q9Yhb+fAcfOBuPBvCpswH8xMlR3FhN4fJSEmvJahfgQFDB49MDOD8bO7AL0DAMXF9N4Qc3N5AtavDKEl45PY5zMwMPFQ+fOzKEdEHFB4sJfOPyKoI+D2Zrol8Nw8BGuoDrK+VrSOWr/xbRgBenJ6PQdAPv34/jB7c2cHQ0jGADP2uzVE2HV2asGRER2Vciq2KnEqc920aM+n5pCkyJ6i5JkvD80RH82ftLuLSUwHNHhruyriEicoKuFv7eeecdfOITnzD/WwxZ/rVf+zV85Stf6ea3pj701r3ybL9zM7Gev3GLRVlJM2AYRt1Ni6LZ8cdNDcHb4HxEIiIiatyDnXLh71AbMZ9AdQ5Npwt/mm6YMZnif51IXHsza0+/14Pzs4M4PzuI9VQeV5aSuLaaRDKn4sd3tvDO/DaenBvEs4f33ozazhTxnevr5gzH0Ug5ZnQ08ujcKkmS8MqpcaQLGu6sp/HnHyzjbz47B8Uj48ZqCtdXk9hKF6vXpsg4OR7FmcmoOYdQ0w0sbmexmS7iB7c28JnHJ5v9NdWVyKr48d1NXF9NYXowiFdOjWF8oPUOVSIiom5Z2C53+00NBhCo0+nfiGrU5x4z/rhn1HVHRkIYH/BjPVnA+4s7ePn4qNWXRERkia4W/l555RVzWDxRO5bjOTzYycEjS3jmcG+7/YDqjD/dMKDpBrx1FmmMbXiU+F2VdL4WEBERdYKmlyMbgfbm+wEPd/ztd7ipWTm1utmVdXDhL1vpVmx1Tt94NIDxM+UuwNvrabx/P461ZB7vzO/gwwcJPDU3iAuHhhD0eaBqOt6e38Y78zvl9aYs4YVjI3jm8BA8cv1/F1mW8Llzk/jP7z3AcjyPP3x78aFCrleWcHQsjDOTURwZCT/SIeqRJXzq7AT+0zuLuLqcxGNTA20/rtKFEt66t4VLD5LQK/eDSzs5/Ie37uPx6RhePj7CuTtERGQr85X5foc7dKhqr6jPokiJ4p5R10iShBeODuMvPljB+/fjeHpuiF1/RORKvNsiR7i+mgQAnJmMIhpQev79Fbm6KFM1A/XGC7Lw9ygfO/6IiIg6ajWZR7GkI+jzYGyPLrBmiPdp3TBQ0g3zsFO7soVqpKSToz5FAbORqM/9KJVZgGcmo7i3mcGP725hPVnAW/e2cXExjsenB3BvM4N4VgUAHB0N4xOnxxELNbbuVTwyfu7JGfyndxaxnSlCkoDZoRDOTEZxYjxyYOfC9GAQ52dj+GAxgVevreFLLx5uKUI2r2p4d2EH79/fMWdjHxkN4cKhIVxdTuL6agqXlxK4uZbCC0eH8dTcYFtRtURERJ0gut8B4MhouK2v5ffWHxVT0rln1AvHxyIYi/qxkSrgnYVtfPQkZ/0Rkfuw8EeOsJooAGh/AdYqWZageCSomoGipiOIvTdPWPh7lNnxp7Hjj4iIqBPuV06kzw2F2u7QUzwSJAkwjPIsmk6tYWq7/Jwc9ZltIepzP5Ik4dhYBEdHw7izkcEbd7ewkSrg/ftxAEDE78Urp8dwYjzS9L9t0OfBF5+dxfxmFnPDwaYPy718fBR31jPYyap4a3676WisOxtp/PWVVbPDYXowgJePj5rdg4dHwjg/N4jv39jAWjKPH97axKWlBD55ZhyHR6xZ4xMREQHASiJnHqoaj7Z3qMqc8bdHxx/3jHpDkiS8fHwEX724jA8W43j60BAiTBogIpfhqx7ZXknTsZkuF/4motbNBPF6ZKiatm/nmhje7PMyr13wyuz4IyIi6iRxIr3d+X5AeWPE55VRUPVyPGR7e12m2sKfs6M+O9Pxt5skSTgxHsHxsTDubKRxcTGB8agfLxwbNiPCWhHyefHY9EBLnxtQPHjl9Bi+9uEK3pnfwemJKEYa7ChdT+bxV5dWoGoGRiM+vHxiFMdGw48UL2cGg/hbz8/h6koSP7q9iXhWxZ++v4SXjo3g+aPDHYuaJSIiasZCTcxnu+9Fvn06/kTUJ/eMuu/oaBjTgwEsx/N4+942PnFm3OpLIiLqKR4xIdvbTBeh6QaCPg8GgtbVqpUGIitFpBFPb1WJyDCVM/6IiIjaVizpWEnkAXSm8AdUZ9HUzoVrV7Ym3jOvatAcuA4wDAM5MeNP6c4atFwAjOILz8ziY6fG2ir6dcKJ8QiOjYWh6QZevbbe0Lz2TKGEP/9gGapm4MhoCH/7hcM4Pla/Y1GSJDw+HcOvvXwE52djMAzg9Ttb+NqHKyiUnFskJiIi55rfygBARzrQa9dV+q71Dzv+eqfc9VdOL7i0lEAip1p8RUREvcV3GrK91WR5c2tiwG/pKWCfKGCV6m+AqKLjj4s4k1jQltjxR0RE1LaleA66YSAWVBqe/3YQ82R6Rwt/2q7/dt6cP1UzzENdnYr6tDtJkvDK6XEoHglL8RyuLCf3/XhNN/CXH64glS9hOOzD585NQZYbW6/7vR586uwEfvKxCXhkCbfX0/jDtxexkyl24kchIiJqSLZYwnqynDJ1eKT9Q1ViXQU82vXHwl9vzQ2HcGg4BE038ObdLasvh4iop/hOQ7a3Zhb+rIv5BKoLs73iGgRzEeflU0vgjD8iIqLOud/BmE/BX1njdLLbanfhz4lz/sQ1Kx7poU28fhcLKnipckL+ezfW8e7CziMdC0C5I/I719exFM/Br8j4/JPTCCjNF0jPzcTwxWdnEfF7sZUu4j++fR/3NjNt/xxERESNEDGfY1E/wh2YA+eRJTP5aPecPxb+eu8jJ8prmqsrSWzzcBERuQjfacj2ROFv0iaFv/2iPkVRkB1/VYqY8aez44+IiKhdovA318HCnyhqFboU9QkAGQcW/rJq+WcI+tw3Fv3puUEcHQ1D1Qz84OYG/vCdRayn8g99zMXFOC4vJSBJwE+fm8Jw2Nfy95uKBfGrLxzCzGAQBVXHVy8u4e357XZ/DCIiogOZ8/060O0nmGsr7eH1T7FyIJp7Rr0zGQvg2FgYhgH8+A67/ojIPfhOQ7ZWKGnmiRzLO/68IrJyn6hPzvh7hLeBiFQiIiKqT9MNLGxl8N3r69hMlaOo5oaDHfv6ZtRnB2O5RcefXIlpd2LUp/gZQi6J+awlyxJ+/qlpfPrsBPyKjNVEHv/xzUW8dmsTqqbj/lYWP7i5CQD46MkxHBltfyZS2O/FLz0za879e+3WJj5YjLf9dYmIiOoxDAP3t8td5kc6MN9PEHP+Hun4qxyyEvsk1BsvHx+FJAE311KPHGQiIupX7ju+So6ynizAMIBowNuRyIV2iBl/DUV9chFnEkVQ3TCg6QY8Dc59ISIicrO8quHeZgZ3NzKY38o8NH9vbjiEUAe70PxdmPEnYjKHIz5spgqPRH86Qbbg3sIfUJ7398RsDEfHwvjejXXcWkvj7flt3FpPIa/q0A0DZ6cGcOHQYMe+p0eW8KmzE4j4vXj9zha+d2MDsaDSkcIiERHRbpvpIjIFDT6vjOnB7h+qYtSnNcaifpyaiOLGago/vrOFn39qxupLIiLqOhb+yNbsMt8PaDDqs8RF3G61vwtV0+GR3bl5RkRE1Kgrywm8em0dWs1ctbDfg2OjERwbC3d0vh9QsznVocKfYRhmoW807ODCX6VLMdjC3Lp+EvF78bPnp3F7PY3vXl9HPKsCAKZiAXz67DgkqfOHup4/Oox4TsXV5ST+8tIKfuW5OYxE/B3/PkRE5G7xbDlhaiTs6+ghZXGoqt6MP0Z99t5Lx0Zway2NuxsZLMdzHS30EhHZEQt/ZGtryXKc1WTMGYU/cxHn5SJOkKVyzJduGCjpjPskIiLazweLcXzn+joAYDTiw/GxCI6NRTAx4O9KgQWobj51asaf6AYDUCnWpJBzYtSnKjr+eMsEACfGI5gdCuKNu1uIZ1V8+rEJeLu0cSlJEj51ZhyJnIqlnRy+enEZv/L8HP8tiIioo1KF8vokGlA6+nVF1Ofujj8x40/xMgmp14bCPjw2PYDLSwm8fmcLX3hm1upLIiLqKlYnyNZWRcdf1PrCnzmrbr+OP57eeoQkSTVz/joXIUZERNRv3l3YNot+Tx8axJdePIyXT4xiMhboWtEP6HzHX6ZS5AsoHkQqUe2ZgvM6/kRcadClUZ97CSgevHJ6HL/w9Iz5b9stXo+Mz5+fxmBIQSKn4msfrKDUwTmURERE6Xx5zRIJdPY9zWd2/D28/mHUp7VeODYMWZKwuJ3FepKz/oiov/GdhmwrWywhmStHCY0PWB/tI4p5xdLeXWuGYUAtidNbfGrVEjMPVZ2bNURERLsZhoEf39nCD25uAijHHH781FhXi321qnNoOlOcEwWzkM+DsL9cNMuqziv8iXhS8TNQ7wV9Hvz8UzPwKzKW4jl8+9oaDIMJEkRE1BnpSsdfpw+zmFGfuw5VqRwPY6mBgIJTExEAwPuLcWsvhoioy/hOQ7YlYj6Hwz4EbDBb5aCoT003zFgrUeiiMq9c/t2VNG7UEBER1TIMA6/d3sQbd7cAAB85MYqPnBjtWdEPqImj6lDHX7am8Ce65ZwY9SmuOaQwXtJKw2EffvaJaciShGsrKbx1b9vqSyIioj4hOv6iXer4q11b6Xp1/AlToqzz1KFBAMCN1ZQ5z5mIqB/xnYZsa03EfNqg2w84uPCn1hS1FJlPrVqicLtTGZxNRERE5aLf925s4J35HQDAx0+P4fmjwz2/Dn+Xoj5DPq85ky1b1KA7bNZvllGftnFoJIRPnBkDALx+Zwt3NtIWXxEREfWDVA87/moTkHhY3DpTsSCmYgFouoEPHySsvhwioq5hdYJsq1r4s36+HwD4KsOX63WtFc2sdgmyzEVcrWNjYQDAtZWUxVdCRERkH2/d28bFxTgkCfj02QlcODRkyXX46sRRtao26jOkeCBJgGEA+ZJz4j513UBOrf4cZL3zs4PmKf2/vrKKRFa19oKIiMjRDMNAptDdGX+1MerisLgsSfBwz8hSYj1x6UECmsMOphERNYqFP7IlwzCwmrBX4U90/BXrdPwVmdVe19mpAQDAg50sEjlu0hARERVLOt69X+70++SZcTwxG7PsWkTcVKcKf7VRn7IsmZ3/mYJzCn/5kgYxSi5og8h5KvvYyTFMxQIoqDq+dmkZpTrrciIiooNkixo03YAkAWFfpzv+ymuHglrT8Sf2jLxSTyPd6VEnx6OI+L1IF0q4tc4D6kTUn1ihIFtKFUrIFjXIkoSxqFOiPln4qycWVDA3HIJhANdXklZfDhERkeWuriRRUHUMhhScm7au6AdUT6Wrmg7DaP/Uc7Ym6hMAwuacP+cU/mpjPpnkYB8eWcJPn59C0OfBerKA79/csPqSiIjIodKVbr+wz9vxDjwzRr1m/0jsGXG+n/U8smQeurt4P27txRARdQnfbciW1irdfiMRn20KaQ0X/rz2uF67OTsVBQBcW0l2ZFORiIjIqXTdwHsL5W6/C4eGLC8sic0pw6ifbNCM3bPxgmLOn1pq+2v3Sm1cKdnLQEDBZx+fhCQBHz5I4BoPlRERUQtS+e7EfAI1M/5qOv6KPCxuK0/MxOCRJawk8lhJ5Ky+HCKijuO7DdnSWrIAAJi0ScwnUD2VpdaZ8ScKf34u4vZ0cjwKn1fGTlbFSqWwS0RE5EZ3N9NI5FQEFI8Zh20lj1ydNVPsQNynKPyF/eWimSieOSnq0yxeMubTlo6MhvH80WEAwKvX1rCVLlh8RURE5DSi4y/i73zhz7dnx195L4mFP3sI+704NVE+oM6uPyLqR3y3IVtaTZYLQ5Mx+xT+vJ7yhli9GKxiqbKI8zIOai8+r4wT4xEAwNVlnswmIiL3em8hDgA4PxszN4asJEmSeR3tzvkzDAPZykZaSClvpIUcGfX5cFwp2c+LR0dwaDgEVTPwl5dWOlK0JiIi90h3teOvvPYplnToenmvSBwWF3tLZL2nDw0CAG6upc1CMBFRv7B+p4FoF8MwsFYp/I0P2GO+H1A9lWUYe3f9ccbfwR6rdDXcWEvVjUwlIiLqZyuJHJbiOXhkCU/ODVp9OSaRbNBu8aSo6ShVNrhE1KconolimhMw6tP+ZFnC556YRMTvxVa6iFevrTFOnoiIGpYuqACAaBc7/oBq159YY3HGn31MDAQwMxiEbhj48EHc6sshIuoovtuQ7exkVRRLOhSPhNGwnQp/EqTKway9ilYs/B1sdiiIgaCCYknHnY201ZdDRETUc6Lb7/RktCvRUq0yI6naLPxlK3GePq9sfk1RPMs6quPv4TmFZE8hnxc/fX4KsiTh+moK11dTVl8SERE5RDdn/HlkCUqls0/M+eOekT09Ven6u/QggRIPqBNRH+G7DdmO6PYbi/ohy/aJQJAkyVyg7VX44+mtg0mShLNT5Qz1ayuM+yQiIndJ5FTcWi8XJi4cGrL4ah7m32MWTSuy6qOz8RxZ+FPZ8ecUM4NBvHCsPO/vnfltdv0REVFDujnjD6geqipo5TVFdcafffa5CDg+FkE04EW2qOHmGg+oE1H/YIWCbEfM95sYsM98P0Ex5/ztMeOPp7caIuI+F7aySOVVi6+GiIiod96/vwPDAA6PhDAWtU+qAdC5jr9cJc4z7K8t/Dkx6lPM+GPhzwmemhuEzytjM13E/e2s1ZdDREQ2ZxiGOeMv6le68j3EnL9HOv5sMN+ZqjyyhPOzgwCAi4txHiAior7BdxuynbWEnQt/9Tv+eHqrMYMhH2YGgzAMMI6JiIhcI69quLJc7na3W7cfUO34K5Ta68rLFEREZvX0fLCm488pmynVqE/7xLFSfQHFg8emy4fL3ru/Y/HVEBGR3eXV6kzi2sNKnVRdW1Vm/GlMibKrJ2Zi8MoS1pJ5LGzxABER9Qe+25CtaLqBjVQBADDpuMJfZRHH01sHEhsz11aSjtkAJCIiasflpQSKJR2jER8Oj4SsvpxH+HZtTrVKFMxCNVGf4UrhT9ONtr9+r+z1c5C9PT03CEkC5jez2EoXrL4cIiKysVShnD4U8nng7VIhbneaQsk8LM49I7sJ+jw4PzcIAPjBrQ3oOvepiJxoPZnH5aUE95or+G5DtrKVLqCkG/ArMgZD3YlbaIevgcIfF3EHOzEegeKRsJUuYi3JjRm7yBU1aFzgEhF1nKYbuLgYBwA8fWgIkmS/dACfp1zgajvqU61EZNacnvd6ZHPzywlz/lRNN38PQUZ9OsZgyIdjYxEAwPv349ZeDBER2ZqI+YwEutfZb0Z9lsSMP7FnZL91IAEvHB1G0OfBVrqIS0sJqy+HiJqk6Qb+7OISvnV1DUvxnNWXYwusUJCtiCLQRDRgy00xxVu+pmJpjxl/JXb8NSqgeHC8sjFzdYULKjtYT+bxL394F9+8smr1pRAR9Z1b6ymk8iWE/R6cmYxafTl78iudmfEnoj5DuyIyw2bcp/3n/OXU8s/gkSUzpouc4cKhQQDlVImcA4rMRERkjXShUvjzd6/wt7vjj4fF7S2gePDSsREAwI/vbiGvch1B5CT3NtPmvahIE3Q7vtuQrawmy/P9JmP2i/kEAK/cyIw/Pq0aIeI+b6ymUdrj90m9dXk5AU03cGMthWRetfpyiIj6RqGk4Y07WwCAJ2cHuxYn1S6RalBs8z1ZFFvCuzrlRCHQCcUYcY0hn8eWB9GovpnBIMYH/CjpBj58ELf6coiIyKZEx1+0qx1/u2b88bC47T0xE8NIxIdcUcOb97atvhwiakJtp+52pmjhldgH323INoolHXc20gDsW/jbb8ZfsRLfwNiGxswNhRANeJFXNdzZyFh9Oa6m6wZurZWfe4YBXFtOWnxFRET9wTAMfPPKGnayKqIBL56szA6xI3PGn9pmx1+lo293RKb474wDCn8ijpQxn84jSRIuHBoCAHz4IMEIcyIi2lPK7Pjr3oiZRzv+eFjc7mRZwsdOjgEAPliMY4fFAyJHSORULGxlzf9m4a+M7zZkG5eW4sgVNcSCCo6OhK2+nD35RNTnPh1/Pi7iGiLLEh6fjgEAfnR7k11/FlqK5x6auXRlOclBuEREHfD2/A5ur6fhkSX87PlpBBT7FpLMzak234+zxTpRn37nRH2Kawyx8OdIpyaiiPi9SBdKuLGasvpyiIjIhswZf12M+tzd8ccZf85wZDSMo6NhaLqBH97etPpyiKgBV5YSMIxqFzcLf2WsUJAtqJqOdxd2AADPHx2GLNtzIVTt+Ntjxh/z2pv2zOEhRANeJHKq+e9PvXdzrbwpdmYyCp9XRiKn4sEOB+ESEbVjfjOD1++UNws+eWbctmkGghn12caMv5Kmm5+/u2gWVMo3YdmC/Tv+RNSnuGZyFo8smd217y/u8DATERE9Qsz462bUZ/VQVXldIQp/PCxufx89OQpZknBnPY3F7ezBn0BEltF1A1cqyWUvHS/P6cwWNUeMmOg2vtuQLVxaSiBT0DAQVHB2asDqy6lLFPV2d6cZhlFdxDGvvWE+r4yfODkKAHh7fpuz5Syg6wZurZdjPh+bHsCZySgA4MpyYr9PIyKifSSyKv7q8ioMozwr5NxMzOpLOpBfab/wJ2I8PbJknnIXRCEwq9r/BixbM+OPnOn8bAyKR8J6ssDDTERE9BDDMMzCX3c7/srrCBGjLg6L23XeM1WNRPw4P1tev3/v5gZ0RocT2dbdzQzShRJCPg/OTA5gIFiOcN7KFCy+Muvx3YYsV9J0vDtf6fY7MgyPTbv9gPoz/kq6AXGYmB1/zTk9EcXMUBCqZuCHNxmj0GsPdnLIFTUEfR7MDYXM+NVba2nkHbA5S0RkN6qm4y8+XEZe1TAZC+CV02NWX1JD/J7y5lQ7UZ+5moKZJD28nhNRnzlHRH2y8Od0AcVjHiZ87z5TJYiIqKpQqiYUhHsQ9VnU9PJh8ZKY8WffPS+qevHYCPyKjM1UAVdXklZfDhHVIRoXHpsegEeWMBL2AWDcJ8DCH9nA5eUk0oUSogEvzk5Frb6cfZkxWLuiPmsLgVzENUeSJLxyegySVI6cZIxCb92oxHyeHI9AliVMDPgxGvGhpBtmBCgRETXGMAy8em0NG6kCQj4Pfvb8lGNOdZtxVCW95VPN1dl4j26iBSt/lnFC1Kda/jmCLPw52tOHhgAA9zYziGd5409ERGWi2y+geLqa2GTO+FN1aLoB3RCFP2esDd0u6PPgxWPl2MDX72yiULL/GpbIbZJ5Ffc2MwCAc5VGhuFK4W+LhT8W/shaJU3HO/PbAIDnjgzbfnNM8ZaLeuquGCxxcsvnlR854U4HG48GqjEKN9YZo9Ajmm7gdiXm89REueguSRIeq7xZXl7iqTYiokbkVQ2riTxev7OFayspyJKEn35iCtGAYvWlNax246vVrr/9OuVCSqXjzwHd5NWfgzP+nGw47MPR0TAMA3h/MW715RARkU2k85WYzy7O9wNqZ/zpUGsOj3PGn3M8OTuIoZCCTEHD+/fjVl8OEe1yZSkJwwBmh4IYqhT8ROFvh4U/8G6WLHV1JYlUvoSI34vHp+0720+oF/VZqAxrZrdf614+Poqba2lspov44EHcPKVN3bO4nUVe1RDyeTAzGDT//LGpAfzo9ibWknlspAoYi/otvEoiInsxDAOXlhJYSeQRzxaxk1UfGRz+0VOjmBsOWXSFrfHIEryyhJJuoFDSEVCa73bbt/BXifoslnSomm7r0+45Rn32jfOzMdzbzODOehqvnBrjAT0iIjI7/qJdjPkEqjP+ijXRol5Zgmzj8Tb0MI8s4aXjo/j6pRW8fz+OC4eGutolSkSN03XDjPl8otJMAgAjEUZ9Cny1IstouoG37pW7/Z49MmT7bj8AUOS9C3/i9JadN7HsLqB48PLxcozCj+9umXFhVHZ3I43b62loHeyGFFGeJyciD918BH0eHBsLA6hmZRMRUdmDnRxevbaOq8tJLMfzZpEo4vdiZiiIj50axdNzg9ZeZIv8SjXusxX7RX36PDK8lfearI3jPg3DMAuYjPp0vrnhELyyhFS+xJt/IiICAKREx1+XC3+1BaJ0ZY2ksGjkOCfHIxgKKcirGi4txa2+HCKqmN/KIJUvIejz4MRYxPzzoVC58JfKl5B3QNpMN7HjjyxzrdLtF/Z7cG4mdvAn2ICI+nxkxl9lg4yFv/acm47h0lIC68kCfnR7Cz/52ITVl2QLdzfS+OrFZQDl7oPHpgdwbjpmtrG3QtMN3N4ox3yeHH90tubj0zHcWkvj2koKP3Fi1BGFeSKiXliK5wAA04MBPH1oCINBBbGQYp7qdjKfR0YGWttRn3sVzCRJQsjvRTKnIquWEIM9Y1ALJd08ZBNqoeuR7EXxyJgZCmJhK4v5rSxGIkwxICJyO9Hx1+2oT48sQfFIUDXDjBflnpHzyLKEZ48M41tX1/Duwg7Ozw7y35HIBi4tlRsVzk4NPLRnGVA8iPi9SBdK2MkWMRUL1vsSfY+vVGSJ2m6/Zw4PO+ZNs17Up/hvZrW3R5YlfOL0OIByp9mlBwmsJ/OuHqKcVzV85/o6gHKUbLao4Z35HXzl9Xn80TuLuLaSRKmFDdr721kUVB1h/8Mxn8Lh4RCiAS/yqoa7lUG5REQELFcKf6cnB3BqIorxgUBfFP0AwFf5OQotnowUhb+wf+/fh4jOzBbt+74urs3nlXnopU8cHimnGCxscT1DRERAuqAC6H7HH1CN+xTf08fxMI50dmoA0YAXmYKGK8tJqy+HyPVSeRX3KnuVT+zRTCTm/G2l3Z34wY4/ssT11SQSORUhn2fPJ6hdicLf7kKLOBnPrO/2TQ8GcXZqANdWkvj2tTXzz0M+DwZDCmJBH85MRnFkNGzhVfbOa7c2kcqXMBhS8KsvHMLidg6XlxKY38rgwU4OD3ZyeM3vxc89NY2JgUDDX9eM+RyP7jljQJYlPDY1gDfvbePKcgKnJh7tCiQichtdN7CSyAMod/z1G7GOab3jrxL1qex9i2EW/mwc9VmNK+2PYi4BR0ZC+AGApZ2c7edLEhFR94nuu2iXO/6AytqqAKQrax++BzmTR5bw3JFhfOf6Ot6Z38YTMzF4OKuRyDJXlpMwDGBmKGgW+WoNR3y4v511fdQ/C3/Uc4Zh4J35HQDAM4edNRjXZ3b8GdB1wyyYcMZfZ71yegx+RcZ6Mo94VkW2qJn/txzP49pKEq+cHsPTh4asvtR9JbIqNtIFAI/O5VM8MmaHQvsuFhe3s2br+k8+NgG/14MT4xGcGI8gmVdxZSmJK8sJpPIl/NE7i/iZ89M42kBBtKTpuFOJ+Tw1Wb+g99h0ufC3sJVFMq9iIGDPWDYiol7ZyhRRLOnweWWMhvsvMtDvbXfG3/6z8cTsPzvP8RUzG8N7zCkkZxoO+xANeJHKl7C0k3PN4TEiItpbqtCbGX9AdW3FqE/ne3x6AG/e20IqX8K1laRjRhYR9RvDMMzO23rNRCOVYiALf0Q9lsyVsJ0pwiNLeGLWWW+USk0sg6rr8MvljS0R9akwtqEjAorHjPwEynGXyZyKeE7F3Y0Mrq0k8b0bG8gWNbx8fASSZI/fu2EYWE3mcXcjg7sbaWwe0FI+MxTE589P77lBWizp+NbVcsfjk3MxzA6FHvr7gYCCl46P4MLhQfzlhytY2Mrizy8u45Nnxg98XomYz4jfi+lY/Y6VwZAPs0NBPNjJ4epyEi8eG9n36xIR9TsR8zk5ENizW9rpfG0U/nTdMIen90PUZ73iJTmPJEk4MhLGpUpiAgt/RETuVSzpKKjldU63Z/wB1bVVplJsVBx08J0e5vXIeObwMH5wcwNvz2/jsamBvrwfILK7zXQRyZwKn1fGifHInh8zzMIfABb+yAKLO1kA5U0zp83E8cgSZEmCbhhQNQPigJha2SDjIq47AooHAcWD8YEATo5HMBRS8PqdLbx1bxvZooZPnRm3bMGVyqtYSeQxv5nBvc3MQ5uZsiRhLOqHd49r20gXsLSTwx++fR+/8PQMBkMPt6b/+O4WEjkV0YAXHzkxWvf7+70e/PxTM/j2tTVcXS7Ho6byKl7apyBqxnxORA4smp6biZmFvxeODtumyEpEZIWVRLnwN73HbNR+0E7hL6tqMAxAkoBAnfVd0EGFP0Z99pcjoyFcWkpgYStr9aUQEZGF0pUCnM8r92Q/qjrjr/J9eVjc0Z6YieHt+W3EsypurqdwZnLA6ksich1RV5geDNTtoh6ppPMk86qZ2ONGLPxRzz2oPEFnh5y3aSZJEhSvhIJqlIt9lZSvgpjxx9iGrpMkCS8cG0HI58Wr19dweSmBnKrhc+cmux6boesGNtIFLMdzWEnksRzPIZV/OK7M55VxdDSMY2NhHBkJI6DsfTOxmS7gz95fwk5Wxf/v7UX83JPT5kbySiKH9++X43A/dXbiwBsSjyzhpx6bQDTgxZt3t/HmvW0k8yX85GMTj0SJlmM+ywNwG5nbd2I8AlmSkMipSBVKjPskIldbjvfvfD8A8FfeRwutFP5qZuPVO4wTdkLUp1q+Nnb89ZfZoRBkScJ2pohEVkUsxPUMEZEb9XK+H7BHxx/3jBzN55Xx9NwgXr+zhbfvbeP0RJSHo4l6bHG7XFeY25WMVivo8yDo8yBX1BDPFjE+0J/37wdh4Y96yjAMPNgpn5bfHV3oFD6PjIKqm/GeQE3HHxdxPfPEbAxBnwd/dWkFd9bT+NP3lvBzT03XLbS1Kq9quLeZwd2NDOa3Mo90QUgSMBb1Y2YwiGOjEcwMBRsa8jwa8eNXnj+Er15cwnqygD959wE+e24SR0fD+NbVNRgGcHZqoKGZfeXrkPDy8VEMBBS8em0d11aSSOSKGAn7UdINaLqBkq4jV9RQLOmIBryY2ifmU1A8MkYiPmykClhL5Fn4IyLXShdKSORUSBIw2cDrpxOJzalWCn85MyKz/u2Fk6I+Q5zx11cCigdTgwEs7eSwsJ3B+dCg1ZdEREQWSBVUAL2Z7wdUZ/yVdANAOS6SnO3JuUG8s7CDzXQRdzbSODF+8IFqIuoMXa/WFeaG968rDId9WCrmsJVh4Y+oJxI5Fal8CR5ZwpRDT8uL2MZibeFPKy/iOOOvt06MR/A3Lszgzz9YxlI8h3/1o3nEggrCfg8ifi/Cfi8ifi8Cigf1DmF5JKkc4SpL8FaiXA0YWNzO4e5GGsvxPHTDMD/er8iYjgUxFQtgejCIiYFAyy3jEb8XX3xmDn91eQV3NzL42ocrmBkKYitdRMjnwcdPjTX9Nc/NxBD2e/GXHy5jOZ43u1N2Ozs10PDJtMmBQLnwlyzgZANdgkRE/WilMt9vNOJ3XFR5o8TPVbvGaVSmUCmY7XMAh1GfZKUjI2Es7eQwv5XF+dlBqy+HiIgsIDr+elX4271XwD0j5wsoHjw9N4g3723jrXs7OD528AgVIuqM9VQBxZIOvyJjLOLf92NHwj4s7eRcPeePhT/qKVGVn4zVz+G1OzHHTxT7yv9/JerTpZnBVpodCuGLz8zhqxeXkMqXkFc7v5k4GvXjWCW+c3Ig0NFFnc8r4/Pnp/H9mxu4uBjHUuU58okz4y3HjB0dDeNXnj+EW2tpSFL55sIjy/DK5SKn3yvj0AEnY2pNDARwaSmB1eTeRUQiIjdYTvR3zCfQ3ow/EZEZ9td/7xJRn3lVg6YbDXXI95rZudjhBAGy3pGREH50uxwPZNfHHxERdZeYtRfpUdSnf9ceEcfD9IenDw3hvfs7WEvmsbCVxZEGk5qIqD2L5viwUN3xEsJw2AcA2GLhj6g3RA6vE+f7CaJgWRv1WeSMP0uNRf34tZePYDtTRLpQQqZQqvyvhkyhfjHQAKDpBnSjHIVZ/f+B8agfx8bCODYWQSzY3XhLWZbwyukxxEIKXru1iVMTUZwcj7T1NUcjfowecPqlUROx8tdZS+ZhGAZPsxGRKy1XOv6mYs5dwxykGvXZ/CGabANRnwFFhixJ0A0D2WIJURvGR7Pjr3+NRf0I+TzIFjUsx3MHxgMREVH/EYW/qL83a5BHO/64Z9QPgj4PnpgdxHsLO3h/cYeFP6Ieqc73O/iefCRc3svcYeGPqPtq5/vtN4DT7nx7FP7E/89FnHUUj4yJgQAmrL6QFkmShAuHhnBuOgbFI9mquDYS9sMrSyiWdOxkVfPUDBGRW6iajvVkAQAwPdj/hb9WOv7MqM99CmaSJCHk8yBdKCFX1GxX+NN0wzwsxBl//UeSJBweCePaShILW1kW/oiIXCiV73XH38PrIu4Z9Y+nKoW/ha0sElkVsZC91rVE/UbTDfMwbiPr+OFIee8ynlVR0nRXzlh1309MlolnVaQL5fl+kzHnxmRVO/5qoj4rG2QKoz6pTT6vbKuiHwB4ZAnjA9WuPyIit1lLlue9RvxeDPRoo8gK4nBTO1GfB3XKiRjrjA3n/OUqRT9JKncnUv85PFLeJJjfylh8JUREZAUz6rNHM/4eifr02uten1oXCyk4PBKCYQCXlxNWXw5R31tJ5KBqBkI+D0YaaEgI+zzweWXohoF4Tu3BFdoP72ipZ0S335SD5/sB1WHMe0V9clAz9avxgXKxnnP+iMiNluPl176pwc7OebUbv9KJjr/9N9JEYTBbLDX9PbpNXFPI5+nrf2c3OzwSgiQBG6mCuflLRETuUNJ0c5Zv1KIZf07eC6NHPTETAwBcWU5A040DPpqI2mGmCA6HGrpXkyTJLBBuuzTuk+841DO1AzidTHT1qaXaqM/yGzxn/FG/mqwU/tZZ+CMiF1pJlG8y+jnmE6iuY0q6gZLWXPFPbKSFD+j4E4XBnB07/hqYU0jOFvJ5MR4tr2kW2PVHROQq4sCH4pEeKch1C6M++9uxsQjCfg8yBQ13N9JWXw5RX6vO92u8riBGFW2lWfgj6pryfD9R+HP2ppkZg1XZEDMMgzP+qO9NmIW/Ak+yEZGrGIZhdvxNx5y9hjlI7QGmYhOFP8MwkDWLZgcV/uwb9Sl+hpCy/89AznakEve5sJW1+EqIiKiXzPl+fm/POvt97Pjrax5ZwuPT5a6/S0uM+yTqFlXTsZIo35PPDTd+Tz4SYccfUdftZFVkChq8soQpB8/3Ax6d8adqBgzj4b8j6jdDIQU+r4ySbmArU7D6coiIemY7U0Re1aB4JIxF/VZfTlfJsmRuUDUT95lXdeiVxVCjUZ85W0Z9irhSFv762eHRMIBy4U/nYSYiItcw5/sFlJ59z92FP6ZE9Z9z0zFIUnldkci6c44YUbetxPPQdAPRgBexYOOv4cPh8v37tkv3MfmOQz0huv2mBoPwOnyh490140/8ryRxxh/1L0mSzK6/tYQ73zCJyJ3EycKJgQA8cv+/z5vJBk0U/sRsvIDiOfB3JAqDWRt2/OUa7FokZ5saCMCvyMirGtZSjDAnInILs/Dn712kt0eWHtonUrz9v5Z0m1hIweFKmgC7/oi6o3Z8WDMd2yLqcyeruvLAHwdYUNu+cXkF//zbt3BvM4Ojo2H8o0+fxGfPTT30MWIAp9NjPoHqhtjuwp/ikXsWF0FkhcmBABa3s1hL5vEEYlZfjuM18tpJZAf1Hqud+nO7W467Y76f4FdkpAtAoanCX+Odcp2M+mzlsbbf5/z3X7uGlWQeM4NB/Lc/rTni8UnNk2UJh4ZDuLWWxvxmFlN9HuFLRERl6UrUZzTQ261Qv9cDVRPzBZ19EJ729sRMDPObWVxZTuCl4yOuOCxI1EvmfL8mYj4BYCDgheKRoGoGEjkVQ5VC4EH6Za9DMgzDtuXOZDKJWCyGRCKBgYEBqy+H9vCNyyv4b/7de5AAGID5v7//pQvmA9wwDPzLH95FpqDhi8/OYraJIZx2dGM1ha9fWsHsUBBffHYO66k8/v0b9xHxe/G//9gxqy+PqGturaXwtQ9XMBb140svHrbsOuz6hlrPXtcL4MDXTupPnV7bdHutVO99/r/+2FH8Lz+41/af//6XLgBAS0WaXi24v/Kje9jJqvj5p6ZxbCzS1tdygv/Ln17CN66sIpFTcXws0tDv/P/xjRu4v53FeNSP3/78Y/v+zv/wrfv4H755AztZFSfHIy3/ewN7v44e9Fjb73MEvib3v8tLCXzr6hqmYgH8yvOHWv46rRaZnbSOof5j58em09ZJZC8HvSb/3752FWvJAuaGgviNz53p2eP7//zHH+LV62t7rn2oP2i6gT94rbzv+bPnp3ByImr1JXWNHe7PyF0KJQ2//7270A0Df++jRzHQZFzzf/ufP8Q3r649cn8LNLdf18peRzce682sbVj4o7Z89p//ADdWU6h9EEkScGYyir/6hx8DAGylC/g3P16AV5bw668cd3zU592NNL56cRmTsQD+1vOH8GAniz965wGGQgr+zkeOWn15RF2TzKv4gx/egyxJ+D984rglpxUbOWxgJ/Wud2YwgOV4ft/XTupPTtvQqvc+7/PIKJb0tv98OhbAUjzfUpGmFwvuXFHD73//DgDg1185joDS3xGQ4jVL6PTvvN7Xb+Xfu97raL3H2pnJKAwDTT2e+Zrcv1J5Ff/rD+9BkoD/+mPHW4p33W9NAjRfmLbjOob6T7MHenr92HTaOonso5HXZKGXj+/91j583e8vr9/exJv3tnFoOIRfembW6svpik4dCuXjn5oh9uEHQwr+bpP77q3cfzZ7n9nre8lm1jbOrsCQ5e5tZh56YAOAYQB3NzLmf4uYz36Y7wdUoxmqUZ/l34Didf7PRrSfqN+LkM8D3TCwkbJmzt8///Yt880Ylf+VJOB/fPWWJddzkHrXu3sRATz62klkB/Xe5wu7Frat/vlyTdEPePg5Xe/585XXF5r683ZeH5YT5TXMSMTX90U/oPqaJXT6d17v67fy713vdbTeY+3uRqbpxzNfk/tXNKBgNOqHYQAL2639O++3Jmn28WzXdQz1Hz42qV818pqMPf6uF9dVi8+t/vX4TAySBNzfziKeLVp9OV1hh/szcp/FSl1hroUEwVbuP5u9z7TzvSQrFdSWo6Phh55AQPlJcmwsbP73A/MJ2h/zM3yVAl+x9OiMP6J+JkkSJmMBAMBaMm/JNTRy2MBO6l0vgANfO4nsoN77vN8rd+TPAXSsSNONBbeY7+eWGWDd/p3v9xre7PcG9n4drfdYOzYWbvrxzNfk/nZ0pPzvO7/Z2mtEJx/Pdl3HUP/hY5P6VSuvyb14fN/b4z2Gz63+FAsqODxSLkxcXkpafDXdYYf7M3Kf6ny/5gt/rbw3AM3dZ9r5XrInlYrf+73fw5EjRxAIBPDCCy/grbfe6sW3pR74R58+aVbEUflfwwD+4adOAQAMw8CDnfITdLaFJ6gdVTv+yq8GogDoY+GPXGA8am3hr5HDBnZS73qnBwP7vnYS2UW99/m/8/KRjvz59GCwY0Wabiy4V+Ll17rpwUDLX8NJuv073+81vNnvXe91tN5j7R9+6tSBj+far8/X5P4nNufmt7JoZfpFJx/Pdl3HUP/hY5P6VSuvyb14fDvt/pXa88TMIADgynICmt782sLu7HB/Ru6SK2pm4thsCw1Frbw3NHufud/9p9W6Xqn4wz/8Q3z5y1/Gb//2b+O9997Dk08+ic985jNYX1/v9remHvjsuSn8/pcu4MxkFH6vjDOTUfz+l57BZ89NAgC2MkVkixoUj4TJgf7YNFM85WdyNeqzUvhj1Ce5QLXjz5qoz4MOG9hNvev973728X1fO4nsot77/G/+9NmO/Pl/97NnWy7SdHvBXdJ085DDtEs6/sTvXGj4dy4+Hvv/zs1/0z0+vtl/73qvo/Uea589N7nv4/n/+cUnMRrxwSNLfE12ienBIHxeGbmi1tK6Zr81SbOPZ7uuY6j/8LFJ/aqR12Shl49vK7839d6x0TAifi+yRQ13NtJWX07HWX1/Ru4jmolGIz6E/d6mP/+R12AcvF5v9j5zv/tPq0lGK8cbm/DCCy/gueeew7/4F/8CAKDrOubm5vAP/sE/wG/8xm/s+7m9GMRcLOm4tpJESdfxzOHhrnyPfmcYBu5spBHyeTEVC0ASzxgAFxfj+O719b4abptXNfx/vncHAPB//NRJvLuwgx/d3sS5mRh+8rEJi6+OqLuyxRL+l+/fBQD8+ivHLZl59Y3LK/h/fesm5jezOD4exj/81ClbvKHW8/UPV/DPvnkDiztZzAwG8cvPzeHFYyNWXxbVcXoyipCv+QVlozq9tunFWmk5nutql++P72zhP72ziKV47pHnSL2/O+jPl+P5tl8fHuxk8UfvPEDI58F/9bFjD61v+tnvffc2/tWP7iGeVTE71Njv/Cuvz2MrU8TR0TD+Tz91et/f+Tcur+D//rVrWE3mMTngx5dePNzyv3cnpfIlvLuwg4Gggr/3E0c7+rXJvr724TJuraXx4rERvHS8+cfUNy6v4J9/+xbubKSbfv3qxOtUJ21niljYsl/0VkDx4NREFB65sdfgOxtpJHNql6/K2Rp5rT0+FsE/+nTvH5tOXCe5wYOdbNdnvEuShKOjYcSCSstf4xuXV/D//vYt3N3jNfmbV9bwR+8uIp5VcXIi0tPX3v/p1Vv4Nz+et+R7U++9fnsTb97bxvRgAH/z2bm+u4f4+ocr+B++eQMPdu1vOG3d0wub6YIZU9mvpgeDmOhio893rq/hg8UEnjo0iE+cHm/pa3zj8gr+r39+BRvpIqZjAfzqC4cOXK+349hYpK33soM0s7bpauGvWCwiFArhj//4j/ELv/AL5p//2q/9GuLxOL761a8+9PGFQgGFQnUxkUwmMTc319VFWq6o4fe/Xy3iNHpDQVW31lL42ocrAICI34sT4xGcGI9gZjCIr19ewa21NF4+PoIX+mSjW9MN/E+VQbS//spxvLuwg7fubePpQ4N4pcUXISIn+YPX7iGZU/GFZ2Zbythul2EY+LdvLGArXcSXXjyMsai/59fQjBurKXz90orVl0EN+tsvHMJ4Fxeu7W5AWbFWEjevTiJLEn7h6WkcHmk+RmY1kcfFxR3cXEtD0w0cH4/g556c7sJV2tPV5ST++spqS5/79z56FAOBg29yvn9zA+8t7LT0PbptejCAX37ukNWXQT1yeSmBb11dw2QsgL/1fGv/7q/d2sTb882/RkYDXvyXHz3W0vfshn/z43lspYtWX8aePntuEmenDn6P20gV8O/eWOjBFfW/nzg5iueO9P5gtBPXSf0ur2r4//7gbk9iC+eGQ/hCmwfGv3F5BddWUnX//uxUFJ89N9XW92jWhw/iePXaOs5MRvG5J3r7van3UnkV//r1eaia0fD7l5NcW0niG5ebv1fwyhL+q48fg9/b+8PjVhF7Z/3Mr8j4bz52HHKX6iliffr5J6dxYjzS8tf5+qUV3Fit/97QSb/w9AyOjnYvzraZtVL3jrQD2NzchKZpmJh4uAtqYmIC169ff+Tjf/d3fxe/8zu/081LekRtPKOq6fDI7nkB6pSVRLULIF0o4eJiHBcX4wj6PChVYjCtKA50i0eW4JElaLqBoqZzxh+5zsSAH8mcitVk3pLn9k5WNTem1pJ52xf+UvnyQi8WVMyoVLIvvwVdrM2wYq00EvHj9GS0p9+zHcmcipVEHl/7cAV/89m5hl4jSpqOW+tpXFyMY7VmXTMVC+DFo+5KhDg+HsZTqUHkilpTnzcx4G+o6AcAT80NoljSzbh0u5Al4NxMzOrLoB46UrkpX0vmkS2WWur4Xtgud8nNDgUbiiDSDQO31tJI5UsoaTq8NrmHEBtTx8bC5kxzq60m8kjkVKTypYY+Xqy5AorHnOFIzdnKFLGZKjh2o9KKdVK/yxY1aLoBjyy1tem6n7yqYWEr2/bjzjAMzG+Vu2v2ei3zyBKePTzU1vdoxamJKOJZte8KQLS3aEDB80dH8KPbm/jhrQ0cGwv3VbFrJ1veixkKKQ0fmL29nkZJN5Aran31u9iPYRjmuuT4eATePms0Mgzg5loKBVWHquvwd6Geki6UsJUuQpJam+9X6/mjw+ZefreF/fZ5jHe18Nes3/zN38SXv/xl87/F6axu2l3EsSK2zuk20+UTdZ84M45YUMHt9TTubKTNDSOfV+5q268VFI8MTddQ0sqPGwBQOOOPXGJyIIBba+muRv/tZ74mhkq8/thZoXI44OhYuOVoAiLBirXS6cmoowp/mm7gP7/3AA92cvjqxSX8yvOHEKmzGW8YBi4vJfH6nU1kK+sWjyzh1EQUT80NurJY7/d6uv5aFQsqjEcnW4j4vRiL+rGRKmBhK9v0pmxe1cz4u59+Yqqhwp9hGPifN25D0w1kVQ0DNiiyaboBVStvhHzm8Unb3BN/98Y6Lt6PN3xIQPwMoxEffppdNS15//4OvndjA3nVXgczGmXFOqnfiedfyOfp2vNqJ1PEV16fR05t7tDRbhvpAnJFDT6vjJ89P22bRK+A4sHHTo1ZfRnUQxcODeLKcgLxrIo37m7j43307y/2ek9PDjQck/6//vAuUvmSY99bWlHSDYiMxc88PtF3BU/DMHBrPQXDKK+/Whi/dyAx328s6m97bToa8eMzj7sjYrZWVwt/o6Oj8Hg8WFtbe+jP19bWMDn56C/b7/fD7+9954YY6i46t6g5YuN9YsCPqVgQR0fD+JQ+jqV4DgtbWUwNBmyz4OoUxSMhr5YXwWIhbJeTsUTdJgr5tV0xvVQ7f8aukVS1xHuLn68R1AFWrZWcxCNL+PyT0/jDtxexnSniqxeX8MVn5h5KeQDKG/avXlvHzbVy5Ec04MUTMzE8MRvr6pxHIrKXo6NhbKQKmN/MNF34W4rnYBjAcNjXUNEPKM+xCvk8SOVLyBW1hjtlu6n2PthOKSZi7dTofbqZxMIDmS0TG2v5NgswVuE6qfN68bwSj7tiSTe7C1uxuJ0DAMwMBvtuD4qcxeuR8crpcfzZ+0u4eD+Ox6cHMBrpj9emTKXwF/I1XojxK+V1T6HkzPeWVtQeWlLk/luXSJIExSOjWKok4XXh4b20U35Nnx1iikOruvrI8/l8eOaZZ/Dqq6+af6brOl599VW89NJL3fzWTREFG7vFDTlBrqghUyi/cI+Eq89yWZYwNxzCT5wcxfGx7sRBWEksemtjqhQPF5bkDuMD5ed6Kl9CtthY9FKnqJqOB5UbOgDYyjin44+bUES9E1A8+IWnZhDyebCeLOCvLq9Ar4n1WI7n8O/fvI+baynIkoSPnhzF/+4jR/HCsREW/YhcRsR9zm9lH3qdaMQDc0OiufihYGWzLNtkpG63iI04n1fu2oyWVohElWKD9+ni4+xUvHQas/Dnos1Z2l+xBwed/TX3Se0UBkR3yNxwe5FwRJ1wdDSM4+MR6IaB715fh2F0P2KwF3KVPaBm4gwDlee4mzr+1FL531vxSLZaW3WSeO3uVj2l1XU2VXV9RfzlL38Z//Jf/kv863/9r3Ht2jX8+q//OjKZDP7u3/273f7WDfNVCjbiSUmNE91+saDiqk3t2mKxeNzwBpPcwu/1YDjsA9D7rr+lnRxKumEuMjMFrek5VL1mbkK56DWSyA5iIQU//9QMFI+EuxsZfO/mOnTdwFv3tvFH7zxAMqciFlTwy8/N4dkjw317Q0ZE+5saCMCvyMirGlabjDEXm8zNnkQOmYW/3h6gqkccUvLbbK3S7AFdJrG0L6C4b3OW9qf2oKAuyxL8bT72NN0wN4nn2B1CNvHxU2PwyhIe7ORwcy1t9eV0hDi0FGzisKTTu8lb0YtDE1ZTmkxmaEamUMJ2pjzfb2aQhb9Wdf3R98u//Mv4Z//sn+G3fuu38NRTT+HixYv4xje+gYkJ+8z1MLu3NPe8AHWKKPyNRvujZb1R1ZtQg5v65EoTla6/tWRvO+7uVWI+j41GMBAsR2PZvevPjPrss0x3IieYjAXw2XOTkCTgg8UE/u0bC/jR7U3ohoEzk1H87RcPuXKOHxFVybKEIyOVrr/NzAEfXVU736/pjj+lvFlml8NLBdWehT8foz57LuB13+Ys7U88r5QuP6/afeytp/IolnQEFA/GXLY/RfYVCyp47ugwAOAHNzf6IupSFP7CzUR9Vl4/Ci4aseWGveJuJiguxcsHOUYj7c/3c7OePPr+/t//+1hYWEChUMCbb76JF154oRfftmHVCjU7/pq1WZmvNVrp/nELEevJGX/kVmLO31qTJ+PbtVDZkDsyGsJopPy6Y/c5f9yEIrLWifEoPnpyDACwnSlC8Uj4qccn8NlzkyzIExEA4PBIuTtkfivb8Oe0Mt9PEB1/GZsU/sQBWLu9Jvq81XuuRvC+rH27Z60RVTv+upuM0G5H0P2tasynJDHFgezj2cNDiAUVpAslvHVv2+rLaYuq6eb+RrCJwp8bO/7UUv+vScTeeKOR7M0QqRozjPlsS/8++prga3J2AFW5tePPPH1a86bXzy/mRLvVFv56lVWfyKrYyaqQpfIMURE3av+Ov+rcHCKyxoVDg/jIiVEcGwvjV184jMenY9wUIiKT6PhbS+aRKTQWv9nO3BFR+MvZJOpTROvZba3i81SLUI3gYav2dWrWGvUPcUC+28+roK/89XMtFgYWGfNJNuX1yHjldPkQ4nsLcWyl7b1/sSmHt7MAAFSJSURBVJ9sofz89MpSU/G/riz8uWDusHhf6MbotCXzNZ2Fv3b076OvCd1sTe1nhmFgO1Pp+Iu4q/DnFY+Zku6KF3Oi3caifsiShGxRM18Hum2+EvM5PRiA3+vBSLj8umP3jj8RZ8HXCCLrSJKE548O4+efmjEPDRARCWG/1zzUJNYbB2l1vh8AhCpzcbI26fiz7Yw/rzhJ3tiGUpH3ZW3rxKw16i+9mlNVjfps/nFX0nSsVGLh5oZZ+CP7OTYWwbGxMHTDwPdubPTs8HSnZdXygaWQ39vUIUpzfqwLoz7FWqYf1TbFdFK2WDITBmcG+ZreDq6IUX2gqi56AeqERE5FsaTDK0sYrMzacovadma1ciPazy/mRLspHtmMxerVwlVsxB0ZLZ/KN6M+M0VbL5x5+pyIiMj+joxW4j43D477rJ3v10oEkej4s0/hrxL1qdhrrdLsjD9xX8Y1V3s4549q9SquTnQEFVp43K0k8ijpBiJ+L4ZC7tqbIud45dQ4vLKE+9vZpqLF7USsW0JNxHwC1SjxVp7fTmXuA3nsFaPeSd1qpBLdfqMRX1ORsvQorohRvTEosOOvKSLmczjigyy7q+glbkJrb9YZ9Ulu88rpMXPhenUl2dXvVdJ0M1JLFByHwj5IEpArarbZONtN0w2UKvNR7HaKnoiIiKqOVg4WLWxnoB8w26x2vl+kyfl+QG3Upz3WL9WOP3ttrije5jaUqiMY3HVv2mlujGSj+syEoy7fy5idpi1EzC5uc74f2V8spODJuUEAwGu3N219eLkeEfXZbOHPjR1/ZpNIH69JxDqt0QNajXoQF3H67PZrF3chUVOhdtELUCeItlu3xXwC1Re3bGUuhySVM66J3GQw5MNLx0cAAD+4udnwTJxWLMfzKJZ0RPxejFVecxSPjFil29iucZ+1CyDGThEREdnXRDSAoM+DgqpjOZHb92Pbme8HwDy9nC1qttj4K9o06lOsnTTdgHZAMRaoFih4ILM9AUZ9Uo1eRehWC87NP+4Wd0Thj5vEZG/PHRmGX5GxmSrg+mrK6stpmtgDFZHljWqno9epzDWJzdZWneTrUsefWGe3kqpBD+vfR18TxMmlTmfS9jvR8Sfi9txE3ExmKqddfF6ZJ8vIlS4cGsL4gB95VcP3b2507fuImM/DI6GHnmsjlSLgZsaeA7JrYz7d1hlNRETkJLIs4XBl03jhgAiudub7AdUNM90wbFFcKdg0lry20NDIaXK7FjCdxizAtNB5Rf2n2KuozxYjZgslDauJ8r0gu0PI7oI+D549PAwAeP3OFkoO24duPeqzkrTnooYbN8wd9lVGXnWy8JcrathMidd0Fv7a1b+PviaItttOV6j73ZabO/4qjxlx2qWfX8iJ9iPLEn7y7ARkScKN1RTubqS78n0Wds33E0bD5YMH2zbt+CtolcMBfI0gIiKyPbHOuLeZqfsx7c73AwCPLJnFFXE/YSVxAt9uUZ+yLJmpKo0c0i2y468jqh1/LPxR7+LqRCd0s4chlnZy0A0DgyHFTIMhsrOnDw0i4vcimVPx4VLC6stpSquFP7HmKZb0hjr4+0HRpoeqOkmst4pa5/5NlyoxnyMRX9OdpfSo/n30NcFsTS2548WnE1RNx062vNE+4sLC3+4Zf7y5JDcbHwjgwuFBAMB3rq+j0OHTwcm8is10EZIEHNoV3yJef7Zs2vFXUPt/sUdERNQvyskCwEaqgPVkfs+PaXe+nxCqifu0WsHGnXK+BufHGIbRs1lk/U50XhVs0I1K1iuWqilH3SQKzrkmC86LlUi4OXb7kUMoHhkvHiuPTHnr3nbH90+6qdWoT79XhghucsuhEjfEjze6RmuGSNWYGWS3Xyf076OvCeKBWmDHX8O2M0UYRvlUVrjJkx79QLxwi5Ec/fxCTtSIF4+NIBZUkMqX8PrtrY5+7YXN8hv/VCxgnhQThisdf5vpoi1m5OxW5AYUERGRY4R8XpyaiAIAvnVtDfoep9Lbne8niO6WZje5u8Es/Cn2W68oDc6PUTWD92Yd4ldai1yk/iQ6/ro+46/FqM/Fbc73I+d5fHoAQyEFuaKGdxd2rL6chrXa8SdJUnXv3SVxn9XCX/+OfGl0jdYM0fHH6ObO4IoYNQ9Ul7z4dEJ1vp/flbPtdg9n7ecXcqJGKB4Znz47AQD44EEcy5U3606ozvcLP/J3w2EfZElCsaQjVbA+Kms3M96BG1BERESO8PFTY/ArMtaTBby/+OhmXLvz/QQ7dfxVZ+PZ70CnuO86uPBX/ntJ4r1Zu8yoTwd1oVD39CpCt5UowFyxGr3MWVDkJLIs4SMnRgEA7y3sIGPDvYy9tFr4A1ov7juVSBXs570gX4cLf52I06eH9e+jrwlmayo7/hq2mRYxnz6Lr8QaivzwzSS7eYiAQyMhPDY9AMMAvn1tDfObGWykCsgVtbrdeCVNR6ZQwk6muOdga003cL9yivPo6KOFP48sYShcnuVgxzl/RRufoCciIqJHhf1efOzkGADgx3e2kMiq5t91ckPCLPxZvNlnGIYZM2bHexq/p7EYKfH3ikd25cHUTgoorc1ao/7Tywjd2qjhRqMPxUGM0YgP4Tail4mscGI8gslYAKpm4M17nU1N6gZNN8yiXSuz1wIu6yYvuCD9SWlwjdYoEac/FFLaitOnKv4WwY6/VmxWbnjHXDjfD3j0tBvjZIjKPnZyDPObGWyli/jT95fMP/fIEsJ+L0I+D0qajkJJR17VzOgYoBx59cRMDOdnY4gGysW8lUQOxZKOoM+D8ejerzcjYT+20kVsZQo4skdx0Epm1CdfI4iIiBzj8ekBXFtJ4sFODq9eX8PfeHoGkiR1bL4fAASV8udb3fFX1HQzItOOM/4Ub7mId9AhXZVrro5x2+Ys1dfLCF1ZluBXZBRUHXlVR6iBM+aLO4z5JOeSJAk/cWIUf/zuA1x6kMSFQ0MYbOSBbxEx30+WJLMzvBl+t0V9llw0469DjVRLO4z57LT+ffQ1QdwclHRjzzkO9KitTDXq040ejfrkU4kIKBfvfv6pGRwbC2M06jfn12i6gWROxWoij810Eal8ySz6iUimXFHDW/e28b+9No+//HAFy/Ec5ivz/Y6MhOqe3hadx5s27vjr51NeRERE/UaSJHz67AS8soSFrSyur6YAdG6+HwCE/ZWOP4uLK2IDziNL8Mr265Tzearxf/spcM3VMYHK75Adf9TrCN1mowDvb7HwR842NxzC0dEwdMPA63fs3fWXq4n5bKWz3m2HStQexSRbSbwvqCWjbspXM8Q6mzGfncOOPzx8c1DUdARk+802sJNssYRMQYMklU+7utHuRS9vMImqJmMB/PxTM+Z/lzQdmaKGTKGEbFGD4pEQUDwIeD3wKzL8XhmGAdzdTOP9+3E82Mnh5loKN9dSkCsLyr3m+wkjldehLRsW/uwcnUVERET1DYV9eP7oMF6/s4Xv39zAkZFwx+b7AdWoz1zR2qjPgirm+9kzItPcVNL231BywwZbr4jN2UJJg64bkG1YEKbeqH1e9eL1IejzIJFTkWugMJDKq9jJqpAkYGaQm8TkXC+fGMH8VgY3VlN4am4Q0zZ9PGcqhb9gC/P9gJr5sS45VOKGJAKx5tINA5puwNvGAZG8qmE9lQfAma2dxMIfyqcbPbIETTfKhT+Fhb/9iM31WFBx7Wa2Iu/u+OPNEFE9Xo+MWFBGLKjU/RhJAk6MR3FiPIr1VB4X78dxYzWFkm5AliQcHqm/wTZS6TzezhRgGIatNq3MGX8ufa0kIiJysmePDOPmWgqb6SK+dW2tY/P9ACDos0/UJ2DftYoZI3VAx58oDLr1/rSTxH6IYfBgtNuZ6SU92riuFgYOfl1c3C53hkwMBLiHR442Hg3gsakBXFlO4tVra/jVFw7DY8MDFyLqUyQWNMvs+GtwhqeT6brhinVJ7XuDqhnwtvFSvJLIwzCAwZBijv6h9vXvo69J4onIOX8H20i7O+YTKOfP1xb7+vkEB1GvjUcD+KnHJ/H3PnoUHz89hp85P7Xv8OjBoAKPLEHVDCRz1p6a382MnfLwZpSIiMhpPLKETz82AUkC7qynOzbfDwBClQ0wqwt/BVWkE9hzrSLus9QD5scUzVk69tssdRqPLJn7I26JZKO9FbXePq+qUZ8H78uZ8/04C4r6wEdPjiHk82AzXcQ789tWX86exHpFzChuljnjzwUdf7Uz7/p5XVK7N37QAa2DiFQNdnB3FqsVFaI9tVMDKfvZZuWkq5ir5Va1MTKMlCHqvJDPiwuHhnBiPLLvx8myZMYOb1bmj9oFZ/wRERE521QsiCfnBs3/7lT8kIjKKpb0A4ta3VSweTqBmK1+0H160QWRWr3k55w/Qm0nbW8OBpgxsw0UnFfinZu5SmS1oM+Dj58eAwC8eW8b2xn7jTERhb92O/4KLuj4E+s6WZJs2b3ZSZ2qpyyZc7R5mKOTuCqu8NUMpKT9bVXegMZc3PEHsPBHZCd2nfNn9/gsIiIiOtjLx0cQDZRPuB8a7syGhN8rw1vZDLKy688s/Cn2XKuIQt5BJ8l52KqzzEg2dvy5Wq87acXr0EFRgIZhIJUvJ70Mhdx9IJ36x+mJKI6MhqDpBr59bQ2GYa/9aTGTONT2jL/+f18RhyYUr2SrUTTdoDSYzLCfQknDWrJzcfpUxVVxhTk7QOv/F6B26LqBrbTo+HN54a/mptLn7e8XciK7E69H4vXJLrgJRURE5Hx+rwdfeGYWP/nYxIFJBI2SJMns+stZWfirbMD5bRr12eiGkmpGEnLN1QlumsVE9YnnVa/uZaoF5/2f74WSjpJe3lhvtfuIyG4kScInT09A8UhY2snhynLS6kt6SKbQbtSn6Pjr/07yXs9HtZLYG2+n8LcSz0M3DAwEFcSCnO/XSf3/CGyQ2ZrKjr99JXJqeWCnLGHQ5U9GpaZdmzeYRNYS0cNbNovEKLDwR0RE1BcGQz6cm4l19OS2mGGcLVo3o9ju6QTmAd0DNgp7XaDod9XOjP7foKX6eh2hG1QaOwyRKZRfMwOKB17uxVAfiYUUvHR8BADwg1sb5mPdDrJqe1Gffld1/LlnTeJvMJlhP6vJPABgZjDQkWuiqv5/BDaoE62pbrCVqXb7yX2eU3yQ2mKfG17MiexsNFzu+NvOFKHr9jnAwY4/IiIiqkfEZVka9anae63ia/A+vRpJaM+fw2kCXkZ9Uu+fV412morOI3b7UT96em4I4wN+FFQd37+5YfXlmLKVImSw5ajPakev3WJMO63oohQCpZKA186Mv3hWBQAMh92dLNgN/f8IbJCvwaHhbreRKnfTiO4aN6uN+nTDizmRnQ0EvVA8EjTdQDynWn05AICSpkOrFCHdEPFAREREzQnaofBXsnfHX3VDaf9Nwl53JvU7zvgjoCZCt2dRn411mmYqXdJhX2uRg0R2JssSfvLsBGRJwo3VFO5upK2+JOi6gZzo+GvxeSfWGZpumFG9/cpN8ePVRqrW/03j2XKtYSjk7mTBbuj/R2CDzJOELsgabofo+Bt1+Xw/APB5GPVJZBeSJJmng+wy56/2IAk3oYiIiGi3asefdVFehZK9Z/z5GoyQYspCZzHqk4CauLpedfw12Gkq4g/Z8Uf9anwggKcPDQIAvnN9va0YxU7IlzQYBiBJ1UjeZvk8MuRKXHq/Hypx05qkEwmK4vB+jIW/juv/R2CDxJOxwI6/fW2myhvqYyz8PRz1yU19IsuJTuTNtD3m/NUu9twejUxERESPEjP+Dppn1U1mx59iz/sZ0Wl00IaSOGmueLjm6gTR8Vc4IHKR+lv1fqY3zyvxuCuWqskpe0mbhT92/FH/evHYCAaCClL5kuWRnyJeN6B4Wt7bkCTJNYdKqocm+n9N0ugs5nryqmaugweDTBfsNHuu7i2gsOPvQKqmm1V4Rn0+XPjjDSaR9UYrr0vbGXsV/uwanUVERETWssOMP7uvV8QBS003UNqn+FesFKjccLq+F6qbsyz8uVnRLKj35nlV+zq0X9FZvGay8Ef9zOeV8VOPTUCSgMtLCdxaS1l2LaIwE25xvp/glkMlxVJvXzutZCYztNhIJeb7RfxeruG6gL/RClG4aSeTtt9tZ4owjPINKhdY1RdwWZLgYTcPkeVGRNRnxh5RnwUXxTsQERFR88zCn4XFlYLNC2a1ySr73auLv2MSS2f4zchFHox2M3Ewvleb17Ismd3H+3VCmx1/nPFHfW5uOIRnDw8DAL59bR3JvGrJdYi5msE2n3OiuN/v7y2unPHXYiPVTmW+H2M+u6P/H4ENMltTtf4+ddCOjUrM5whjPgFU4y4UrwRJYuGPyGrDlY6/nYy674nwXjELfy5Y7BEREVHzgpXCX87KGX+q6Piz56wsWZbMQ7r7xUiJk+Z2LWA6jejKYMefu1nxvBLzw/L7PN8544/c5KXjI5iMBZBXNXzj8ir0fWJwuyXb4Y6/fn9vcdeMv/YaqUTH31CIyYLd0P+PwAb5zAo1O/7q2UiXC3+jjPkEUD3VwE19InuIVqIBdMPATtaak3C13LTYIyIiouaJGX/ZombJRl5J01GqfF+7Rn0C1fuuejFShmG46nR9L9TOYTIM7pG4VXVOVe+eV40UBkQRIsIkKnIBjyzhc+cm4fPKWNrJ4e357Z5fg+jADbZd+Cu/lvR71Keb1iTtNlLFKx1/g+z464r+fwQ2SDxQCzboErGrhc0MAGB2KGjxldiDV64U/mx8k0zkJpIkmQcTNtPWx33y5DkRERHtJ1TZ4DYMIG/BJphIJ5AkZxT+1Dr36qpmQNSm3LDJ1gui+KIbRstze8j5ij2O+gQOni9ZKGnmdYUY9UkuMRjy4ROnxwEAb9zdxnI819PvL6I+233OiXSBQp9HfRYtODRhlXYbqeI50fHHwl839P8jsEHtZtL2u51METtZFR5ZwtxwyOrLsQWRPe+GF3Iip5iKlQ8mXFyMW346WdyQ2jU6i4iIiKwly5J5ej67zzyrbqlNJ7Dz6ALzNHmde3WxwSZJ1cgpao/ikeGtzLHv91lMVJ8VBxkDB8yXzBaqc0l5wJLc5OxUFGcmo9ANA391ebWncZmi4y/UZsef2Ee14rBTL4nYS8Xb/2uSg1IZ9mMYRnXGX5Dpgt3Ad8mKg04Rut3dmm4/bmKXzQ2FcHYqimePDFt9KURU8czhISgeCauJPO5spC29FkZ9EhER0UFC5pw/6zr+7H6Q0XdQx19NV5KdC5hOI7r+Cn0+i4n2ZhiG2cHRy4L6QY+7dGW+H2M+yW0kScInzoxjIKggmVPxvRvrPfve1Y6/Ts346++996JD1ledoHhbr6fkVd3s/mTUZ3f0/yOwQQedInS7e5XC39HRsMVXYh8+r4zPnpvCifGI1ZdCRBVhvxcXDg0BAH50e8uSeTmCyK13w2KPiIiIWhNUrOv4E2sVv2Lvg53mWI469+pis8nOcaVOVDvnj9xH0w3ohij89e65dVBHUKcKEEROFFA8+Ny5SciShGsrKVxZTvTk+4rDSeE2C+5+8/28vw+UuGrGn6f1eoro9osGvK74XVmBv9UK8UAt6YalG8V2lFc1LO2U86NZ+CMiu3vmyBCCPg+2M0VcWU5adh3s+CMiIqKDiHk5YjO7lwolZxTMDkrnKVgwh8wNzM6MPt+gpb3Vxrb18iCjOAyRK+79fM9Uoj7Z8UduNT0YxAvHyslj37m2jpVEd+f9GYZhHk4KsuOvIaoFMclWOSiVYT/xbHm+32CIMZ/d0v+PwAbVPhk5vPph97ez0A0DIxEfn4xEZHt+rwfPHy0vhN+4u2VZhHORp8+JiIjoAJZGfarOWKuImEExM2c3N52s76XqBi0Lf25UG/Mpy72P+qz3uMtUoj7b7TwicrIXjg7j2FgYJd3AX3ywjFRe7dr3KpR0aJUGmVCbCQFueV8pumhdIuYYqlrzjVTxSsffYJAxn93S/4/ABnlkCZ7KYsbOhT9dN8wnRq/c3WDMJxE5y/mZGAaCCtKFEi4uxi25hgI7/oiIiOgAovBnRdRnUatEfdp8hvtBYzmKLjpZ30tu6cygvVm1cX1Qp2m18Gfv1y2ibpIkCZ89N4nRiA+Zgoa/+GClaweexfrEr8jwtvl64D8gursfGIbhqvSn2vcIVW/u3zWeKxesh8Is/HVL/z8Cm2BGiNj4Bejt+W38qx/N44MebWTruoH5rXLh78gIC39E5Axej4yXjo0AKL9uWnGirOiQ+CwiIiKyjoj6zFoR9emQjr+DYqRqO5Ooc6oz/vq7M4P2ZlVU3UGzJTMdmjVG5HR+rwc/9+QMgj4P1pJ5fOvqGgyj86OrRLG93W4/4OGOv25cqx2UdAPiR3PDusQrS5ClSiNVk/UUMeMvFmS6YLfYe4XfY+ZJQht3/C1Xspt7FV+3mswjV9TgV2RMDwa7/v2IiDrlzGQUo1E/CqqOt+e3e/793XTKi4iIiFoTtDLq0yGHlHwHdAhUOxft/XM4jVsi2WhvRYtmZwa8DUZ9+lj4I4qFFPzME1OQJQk3VlN4617n9z1ylediqAPPuUDlfdow+rfrr3avXpH7f10iSdJDcZ+NMgzDnPE3FGLHX7f0/yOwCT4xO6Bk31MHicqTIlvU8OGDRNe/373Narefp4e57kRE7ZJlCR85Xu76u3g/jmQXc+/3YsZOuSDXnYiIiFpjZdRnoVSN77Iz5YCOv6LZ8Wfvn8NpzAJMn27O0v5Ui+5lRMG5WDNXrFaaM/6IHjI3HMInz4wDAF6/s4Xb66mOfn2xPgl1IF7X65HNLrhCn8ZIWzUf1UoHJTPsJVvUUCzpkCQgxhl/XcOVcQ27d/wZhoFkvhoB8+7Cdte7/u5ucr4fETnX0dEwZoaCKOkG3riz1bPvaxiGuZBlxx8RERHVUy38WRD1aXb82XtW1kEz/qyKJOx3jPp0N6tmZ/q9MiqpcY889lRNN18HOOOPqOqJ2RieOjQIAPjrK2tYT+U79rWzIurT15nnnFhzFOrM8XS6QiWFwE1rEnHwqpmoTzHfLxpQ2p4dSfXxN1ujlQdqL6ULJWi6AVmSMBBUkCl0t+svmVexmSpAkjjfj4icSZIkfPTkKADg6koSW+lCT75vSTegV4Ld3bTgIyIiouaI6CxVM3p+H+qUqM+DO/6siSTsd6LzqsDCnytZ9bySZcksDOwu/ImYT59XZqoK0S4fPzmGQ8MhFEs6/uKDlY4d2jA7/joUr3vQHE+nE3GXblqTtNJItZMpz/cbZLdfV7nnUdiAg24orJYwq+FevHB0GEB3u/7mK91+07GgOXuCiMhppmJBHB+PwDCAdxd2evI9xY2yJDHqk4iIiOpTPJIZe9XrOX8Fh8wjPihCSvy5mzbZesHf55uztL/q5nXvo+rMwsCuwxDpms4jSXJHhB5Ro2RZws+cn0IsqCCZU/HXV1ZhGO2PssoUO9zxJ+bH9mnHn+rCw0it1FNEjWMozMJfN7nnUdgAu0d9JnPlF9tYUMHZqQGz6+/SUne6/sR8v6Nj7PYjImd7cjYGALi/ne3I4vcgtSdkeVNKRERE9UiShGDlFH1W7W3cp+jkckrUZ6FOR6S4f7d756LTiI6/nKr1ZP1M9mJlhK547O3uWBKdR5zvR7S3gOLBz56fgleWcHcjg7fubbf9NXNmx1+noj4r7+l9eqjEqvmoVhIHRMR8w0bsZMsdf7GgryvXRGXueRQ2wDxJaNOoT1ENHwgq8MiS2fX3znznu/5UTcf9rSwAzvcjIuebHgzCI0tI5UuIZ9Wufz9uQBEREVGjqnP+rOn4s/t6xdxQ0vbeUGLUZ3cEKgVhTTfq/u6pf4nnlRWb1/XmS4qOv3CHIgeJ+tH4QACfODMOAPjx3S1zb7dVnY/67O+OP6ekKXSSeJ9oppFK7MsNhdjx103ueRQ2wO4df6LwF6vk33az629xO4uSbmAgqGAkzOo7ETmb4pExFQsAABZ32lv4NqLokI00IiIisp5Z+Cv0bhNM16szBUWko12Z9+klfc/OM1GUctMmWy8oHgkeuVx07dcNWqpP7IspVnT8mTP+Ht6bEzP+wn57dykTWe3cTAyPTw/AMICvX15BKt/64edsh6M+63X09gs3xo/XrtMaYRiGWeMYDLHm0E3ueRQ2QDwpez1UvVHJ/MOFP48s4fkj3en6EzGfx0bDjKkjor4wNxwCACxu57r+vQqVzRFuQBEREdFBgoro+Otd1GftYVe7x1GJ+3TdMKDpjxb+ipV1lxWzyPqZJEl1O6+o/1m5eV2vMJCpHI6IMOqT6ECfODOOsagfuaKGr19a2fP98yDFkm4erulUx1//R31aNx/VKs3O+MsUNRRLOiSpWuOg7rD3Cr/HDooQsVrSjPqsvtg+Nt35rj/DMKrz/RjzSUR9wiz87XR/zp8b4x2IiIioNWJeVbaHxRWxVvHKErw2L/zVFib3SucxO/5s/nM4kSjA9OsGLdVnbdRnvcKf6Dxi4Y/oIIpHxufPT8OvyFiO5/HDWxtNfw1xIEnxSB3b2+j3qE/Vwm5pqzRb+NvJlOf7DQQUM1mAusM9j8IGVKM+7ffiU9J0M8+8threja6/jXQBqXwJikfC7FCw7a9HRGQHkwMB+LwyckUNm+liV79X9UaZMTRERES0v2AlPivXwxl/Ip3A7jGfACDLUvWQbmmPjj+NB666pRq5aL89Euou1Xxe9X5Tttppuivqs1KEYMcfUWNiIQWfeXwSAPD+/ThurKaa+nwx3y/YwWJ7ved3vzBj1F10GEmsvxqtSYiYz6Ewu/26zT2PwgaIk0x73UxYLZUvwTDKTyYRBSM8Nj2AaMDbsa6/exvlbr+54ZDtT38SETXKI0uYHuzNnL8iO/6IiIioQeaMv14W/lQxj9gZh5TMsRy7NpUMozqr0E3zdHrF3+cbtFRf0Yyrs1PHX/m/OeOPqHHHxyJ4rtIw8u1ra9hMFxr+XLEuCXdovh9QXXcU+rTjz8r5qFYR9ZRCg6PTdrLlg/iDQc736zb3PAobUO34s9+iVlTDBwLeR2bueWQJLxwdAQC8eXe7rZOihmHg5noaAHBsNNLy1yEisqO5ITHnr8uFP01spvFtloiIiPYXUson6XM9nPEnNmecslapd69eO6aDB646r98j2ag+Kw8y7vW4K2m6WQgMs+OPqCkvHx/B3HAIxZKOr32w3HAXt4j6DHaw8NfvHX9Wzke1iuJtbnRaPFuucQyG2PHXbe55FDbAPEXYYIW6l8zCX52hl49PD2A06kde1fD6nc2Wv8/CVhabqQJ8XhknJ1j4I6L+cqgy5+/BTg56C8OtG8WOPyIiImqU2FDL9LDjz4yickDUJ1AzP2bXvbooBEpSeV4hdVa9zivqf1ZuXu9VGBCvj15ZcsyBBSK7kGUJP/3EJKIBL3ayKv76yioM4+D9kGrHXwejPvs8Qrr62umeNUmzM/7iouMvxI6/buO7ZY1mH6i9lMyXC3+xOoU/WZbwidNjAIBLSwmsJfMtfZ+35rcBAOdmYuYin4ioX4xG/AgoHhRLOtZSrb1ONqLAwh8RERE1SMTW5VWtqweTaomILafMI67b8Vez5tqdjEPtC3j7uzOD6lMtjNDdqzCQKZQ7j8L+R1OwiOhgIZ8Xn39yGl5Zwt2NDN64u33g54iOv1BHO/7KX6tY0nu25umlosMSFTrB10Q9xTAMs+NviB1/XeeeR2EDzJsJB3b8AcDsUAhnJqMwDOC719cbOr1Razmew9JODh5ZwoVDg+1cLhGRLcmyhNmhIABgcTvXte9jdvy5KN6BiIiIWhPweiBJgGEAuR6dgHdc1GeddB5RCOSaqzvY8edOmm6gVNmQt+I1QnRBF0s6tMp1VAt/zjisQGRHEwMBfPLsOADgjbtbuLuR3vfjRcdfJ6M+a19TGp0J5yRWzke1SjP1lFShhJJuQJYkDARY+Os29zwKGyBuFkq6YbtTB6LwV6/jT/joqTH4vDJWEnlcXUk29T3ernT7nZ0aQJRPPiLqU3PD3Z/z58ZTXkRERNQaWZYQrBRYsj2K+yw4Nepz12nyooVdSW7Awp871T7PrHhu+TwyRFOfeOyJqE/O9yNqz+PTMTw1NwgA+KvLq9jJFOt+bLYLzztZlsxCUT++t1jZLW0Vc3RaAx1/iayob3ghM6K969zzKGxAbSRbIw/WXkrmyqebDir8RfxevHB0GADw2q3Nhl9EN9MF3N3IQJKAZw4PtXexREQ2Nlfp+FuO51Dq0mt9QWPUJxERETVOxGjlelX4q9wn+r3O6J6pd5q8yDVXV5mz1vqwK4PqE88rjyzBY8HGrCxL5muTWfirifokovZ87NQYZgaDKJZ0/MWHy2b8927ZyvMu2OFRUOahkjrf18msnI9qFTHPUC0ZB6YPiphPzvfrDfc8ChtQu6ixU+Evr2rmYqeRNtinDw1hOOxDtqjhjbtbDX2PdyrdfifGIxgO88lHRP1rOOxD2O9BSTewkujOnL9qx58zNtOIiIjIWkFfeTM7U5mn023iftcp6QTmppL28IaSGzfYeklszhb6sCuD6ivaYF757qJzWhT+fCz8EbXLI0v4mfNTiPi92EoX8a2ra3sWbLJqdzptxdqj0IfzY1UXHkgSP6tuVGOi69nJljtMBznfryfc8yhskBkhYqMTbclKzGfI52nohcMjS3jl9BgA4IPFBDbThX0/PpFTcWO1nOv83JHhNq+WiMjeJEnC3FAl7nOnO3GfdrhZJiIiIucQHX89i/pUnRX1aXb8aQ//ftSS8dDfU2cFvIz6dCM7FNR3x8xmi5zxR9RJYb8XP3N+Ch5Zwq21NF67vfnQ35c03VwrhDo44w/o344/XTfMA0pumj2syNWfdXck+27xHDv+esk9j8IGVW8obFT4yzc236/W4ZEwjo9HoBsGvndjY99W2/cWdqAbBg6PhDAxEGj7eomI7K6bc/4Mw2Dhj4iIiJrS86hPsVZxyMaUuM5i6eH7WlEI9Hk4J6YbRGFY1YyuReST/ZgFdQufV2bHX6Xwly6U/zfCqE+ijpkeDOJTZ8cBAO/M75hpcEC1288jSx1PB6g+v/vrfaX40HxU96xLZFl6KO5zP/FKx98QO/56whmr/B7yNfhA7aVEpRo+0EThDwA+fnIMXlnC4nYWN9fSe35MtljC5aUEAHb7EZF7iMLfaqJQN8++VapmQDfcd8qLiIiIWheqxNdlexT1KdY//g7P7ekW0Xm0+4CuKAQy6rM7/F4ZUmXvknP+3MMOszN3d5uKGX8hRn0SddTj0zF87NQoAOCHtzbNPWJxECnk80CSOlvEEiNR+i1GWnS7yZI181GtVG+dVsswDCTEjL8gO/56gavjXRp5oPZaMlde4DTT8QcAsZCCZyvFvG9cXsU3r6yaTzDh/ftxlHQDk7EAZoeCnblgIiKbiwUVxIIKdMPAcryzc/7E+4ckueuUFxEREbXO7Pjr0SZYoeSsGX+iALF7JIcdChT9TJKkRyIXqf+J9BJLoz594nGnQ9MNswjBjj+iznvm8LDZDPLta2u4vZ4yi+3BDsd8Ao/O8OwXZsynV+54sdTuGklQTOZLKOkGPLKEaICv5b3A1fEu5gPVRi8+ZsdfoPk22GePDOFEJfLzynISX3l9Hq9eW0Myr6JQ0vDBgzgA4LkjQ657USIid+tW3GdtzCdfV4mIiKgRwR7O+KuNJXdK4U8UIHbPjlFtUKDodwHvw5GL1P9sMeOvpuMvU+mE9siSWTAgos76yIkRnJuJwTCAr19aNZPjwl3osu3XAyXVQxPu2wcy12n71FNEM1IsqEB2WUekVVhe3aXeDYWVROGv2Y4/oPzzfP7JaawkcvjxnS0sbGXx4YMEriwnMR71o6DqGA77cHws0unLJiKytbnhIC4vJbC4053Cn4ivICIiIjqI6PgTJ+y7qaQb0PTyqXSnrFf8dU6Sq+z467ryBq3ad7OYqD47PK9qZ4BVYz47HzlIRGWSJOFTZ8aRVzXcXk/j2koSQHc6/sR7esFGTTedYIfXTqv4Gqin7FTm+w1yvl/PuO+ReIBGWlN7yTAMJNso/AlTsSB+8cIsvvjsLOaGQ9B0AyuJcrzds+z2IyIXmhsqd/xtpApmdEwn1Hb8ERERETUipJTP5OaKGgyju/PmxUabLEmOOZVujuSoF/XJjr+u6dfODKrPvJ+xsuOv5nGXKTDmk6gXZFnC585NmulIQPVgUif16/tK0Qbd0lZRvOX15H71lHilvjEY4ny/XunaI/Gf/tN/ipdffhmhUAiDg4Pd+jYd52ugNbWXMkUNJd2AJAGRDuTfzg6F8IVnZvGFZ2ZxeCSE4+MRnJkc6MCVEhE5S9jvxUjEB8MAluKd6/orlMqLV78LF3tERETUGnGivqQbXT+EWqhstDkpllwUKHf/buwwi6zfic4rscal/meHzWuzMFDSqh1/LPwRdZ3XI+PzT05hYiAAABgOd75II6J8C31W+FNdfBjJ5yn/m+43Ok0kGg620dhEzenau2axWMQXv/hFvPTSS/iDP/iDbn2bjjNPEtqk4088KaIBBZ4O5t/ODYceOsFBRORGc0MhbKWLWNzO4cR4tCNfs8COPyIiImqSzyvD55VRLOnYSBUwO9S9e7WCw+b7AdV1lVoyYBiGWbAsujhWq1f8ZmeGPfZIqPtUrdx1bGVHsCg454rVwl/E74xoYiKn83s9+MIzs1hN5DEzFOz416+N8u0n5mEkF65JxPuFeP/YSzpfeS3vQGMTNaZrj8Tf+Z3fwT/+x/8YTzzxRLe+RVeYUZ826fjrRMwnERHtTRyAuLeZ6VisFjegiIiIqBUnx8tz11+7tdnVuE9zHrHinLWKOKCrG9X5hEA1qccpkaVOJDoz+i2Sjeqzw+iCYKXgXCjpyFTGMoR93Cwm6hWfV8ahkVBHm1AEMV+43zrJqx1/7luTiGLnfjP+0oVKcxO7t3vGVr/pQqGAQqFg/ncymez5NTRSoe4l0fE3wGo4EVHHHRoOweeVkcipWNzO4dBI+6fr7TATg/qXHdZKRETUHS+fGMWt9TRWEnncWEt1bSRDtePPOd0zteuqoqbDuyuphweuusdJnRlcJ3WGaqOoz2JJNw/Eh7lZTNQXxMEjVTNQqnlPd7piSXRL98fP0wzfAQmKmm4gWznEwY6/3rHVI/F3f/d3EYvFzP+bm5vr+TX4GqhQ91KCHX9ERF3j88o4O1WO+Ly0lOjI17TDCVnqX3ZYKxERUXdE/F48e3gIQLnrr1v3pOKEvZPWKrIsVQ/plmo6/ioHdnngqnvMWWsO6PjjOqkz7FBQ93lkiBGk25kiABb+iPqF31t9fudtkrjXCaoNXjutclCCYrpQgmEAHlkyO7qp+5p6JP7Gb/wGJEna9/+uX7/e8sX85m/+JhKJhPl/i4uLLX+tVpkVapu88JhRnyEW/oiIuuHcTAwAcHs9bc6PaEfRgXNzyDnssFYiIqLuuXB4CANBBal8Ce8u7HTlezhxxh9Q3VQqaOUClGEYPHDVA2bhzwGRbFwndYYdEkxkWTK7ktOVe7QwZ/wR9QVJqj6/Cw44VNIoc8afCw8jiZ+53qG1tDmr1WvOaabua+q4zD/5J/8Ef+fv/J19P+bYsWMtX4zf74ff72/58ztBOaA1tdeqUZ8s/BERdcN4NICpWAAriTyuriTx3JHhtr5egRtQ1EV2WCsREVH3KB4ZHz05ir/8cAXvzG/j8ekBRDt8L1hQnVn4K9+ra2aXX+09uxs32XrFSVGfXCd1hhn1afFrRECRH+o05Yw/ov4hnt/92PHnxjVJdXTa3v+e4pA9Yz57q6nf9tjYGMbGxrp1LbZwUGtqL2m6YVbEGfVJRNQ952ZiWEnkcXkpgWcPD7V1AqmoOS8+i4iIiOzj5HgEM4NBLMVz+NHtTXz23FRHv75Yqzhpxh9Qc0i3cq8uCoCSBHhlnh7vloDXOVGf1BnVzWtrn1flbtPyYXhZkhDyOes1i4jqE8/vfnpvMWOSXVj4Ez9zbRx7rVS+XN+IMrK5p7r2SLx//z4uXryI+/fvQ9M0XLx4ERcvXkQ6ne7Wt+yIg1pTeymVV2EY5cUWFzhERN1zaiIKn1dGPKticTvX1tdi1CcRERG1Q5IkfPz0GCQJuLaSwkqivbXJbmbHn+KstYo4VCXu1WtjPhkb1T0i6rNY0qHpe2/oUX8x72c81u5DBWpeo8J+D5/nRH1E7JcUHNBN3ihz7rAL94Kqcez7R31yVmtvde2R+Fu/9Vt4+umn8du//dtIp9N4+umn8fTTT+Odd97p1rfsiN03E1YyYz6DChc4RERd5PPKeGxqAABwaSnR1teqzsTggQ0iIiJqzcRAAGcra5Pv39iAYXSu4FKwwfyuVvge6fhz5s/hNLWH2QoOmPNH7dF1w9y8VrzW7kMFler9VIgxn0R9xUnzYxtVnfHnvj18s5GqToJiOs+oTyt0bYX8la98BYZhPPJ/r7zySre+ZUeYramaAd3i02yi8MeYTyKi7js3EwMA3F5Pm/njreCMPyIiIuqEj5wYhc8rYyWRx421VMe+rijeBBza8Vfco+OPukeWJbM71Alz/qg9qm6f2Zn+msJf2M9DlUT9pDo/tn8Kf+6e8bd/I1W6UK5xMOqzt9z3SDxAbVW+aHHXXzJX3ngeYOGPiKjrxqJ+TMUC0A0DV1eSLX8d8d7BqE8iIiJqR8TvxXNHhgEAr93a7FgqTcGMJXfWRvru0+RFF2+w9Rrn/LmHKKjLkmT57MxAzWtUhJvFRH1FrEEKdTrEnMhMInDhXpDvgMJfih1/lnDfI/EAXo8MT2VxY3Xhz4z6DLDwR0TUC6Lr79KDREuRWoZh8PQ5ERERdczThwYxEFSQypfwo9ubHfmaTp1HLA7pivt0N5+s7zUzko2Fv75XG/Np9ciZ2q5kRn0S9Rfx/C700ftK0cUR5NXRaY8mKOq6gUyh/O/MQxy95b5HYgMOyqXtFUZ9EhH11qmJKHxeGYmcisXtXNOfX9R0iHohC39ERETULsUj45NnxgEA79+PY2Er0/bXdGrHX3VTiVGfvRZg1Kdr2Gl2ZkBhxx9Rv/KbneT98b5SewhcceG6ZL8ExayqQTcMSBIQ5iGOnnLfI7EBtVVqKyXzLPwREfWSzyvjsakBAMCHS/GmP99O0ThERETUH46OhvHU3CAA4K+vrCJXbP10vK47N51AFCKKpfJ9erVAwTVXt5kdf6X+6cygvdnp9SHAGX9EfavfOslLumEeAldcuC7xyBLkSpf47rjPTKES8+n3QuY+WU9Z/05uQ+LGoWhhx1+hpJk3dANBVsOJiHpFxH3eWc+YC5RG1d4oWx2NQ0RERP3jJ06OYiTiQ6ag4dvX1lqKJAcenqXjtKhPUYgomh1/xkN/Tt1T7fjrjw1aqs9OszODDxX+uC9G1E/EGqRfZvzVFrvs0DHda5IkQfGKwt/Da1Rzvh9fx3vOfY/EBogFjpUz/pK58pMi6PM4LoKFiMjJxqJ+TMUC0A0DV1eSTX1u0cXDnImIiKh7FI+Mzz4+CY8s4fZ6GleWm1ujCIVKx5bPKzvu1PXukRx2KlD0u0BlT6LQJ5FsVJ+dZmfWzvhj4Y+ov/Rbx59acxjJrYfAq8kMD68V0pUD9Xwd7z3r38ltyDxJaOGpAzHfbyDAmE8iol4TXX+XHiQeGUy8n6I5M4dvr0RERNRZ4wMBvHx8BADw/ZsbiGeLTX8NJ69Vdnf8iQKgG0/W95q/zzZoqT47RX2GfF54ZQl+RUZI4YF4on5SOzu21RQDOylo5fdHN8Z8CrtnMQtp0fEXYOGv16x/J7ch8yShhR1/ovDH+X5ERL13ejKKgOJBIqfi9ka64c+z040yERER9Z8Lh4YwOxREsaTjG5dXmzqgBFQjtRxZ+Nt1n252/DnwZ3Eac4OWM/76np1mZ/q8Mn7pmVl84cKs4zqUiWh/It1ON4xHoiGdSPwMduiWtkq9BMV0oVzjiLLjr+fc+2jcR70KdS8l8yz8ERFZRfHIeGpuEADw5r3thk+gOXkzjYiIiOxPliV85twk/IqMlUQeb97bburza6M+nUbZFSFVLVA472dxmpDPi2jAa0Z+Uv8SszPtsnk9PRjE+EDA6ssgog5TPBI8lYJ+PxwqUXkIvG4jVYodf5Zx76NxH/UyaXspKaI+g3xSEBFZ4elDg/B5ZWymCrizkWnocwqMnCIiIqIuGwgo+OSZcQDAm/e2sBTPNfy5eVUcUnJeAWd31GeBm2w9c3Q0jP/yo8fwuSemrL4U6jI7zfgjov4lSVJN3GcfFP742ll3dJqY8Rdhx1/PuffRuI96ram9YhgGNlIFAMBg0GfJNRARuV1A8Zhdf2812PXHqE8iIiLqhTOTAzg7FYVhAH9+cdm8fzyIk9MJxNwctWTAMAxushF1Ae9niKhXxCGk5Xje4itpHw+BVyOiazv+DMMwZ/xF/Uw17DX3Phr3Ua9C3SsPdnJI5UvweWVMDzLSgIjIKk8fGoTikbCWzGN+K3vgx4sDI7xRJiIiom775JkJTA8GkFc1/On7D7CTKR74OeIe1684b60i1le6YaCkG4zVIuoCFtSJqFcOj4QAAN+9vo7Xbm02PbfYTvjaWRvJXv13LJR0lCr/rmG/89ImnM69j8Z9mCcJLRouen01BQA4NRGF18UvGEREVgv5vHhidhAA8Na9rQO7/oo85UVEREQ94vPK+PmnZjAW9SNT0PAn7z0wZ8XXI2b8OTHqU5Gr6ytV080DV+L+nYjaV+TsTCLqkY+dHMOzR4YAAG/Pb+PPLi45NvZT1BDcfBhJ/Oy1HX9ivl/I52GNwwL8je9hrwdqr6iajptr5cLfmcloz78/ERE97JnDQ/DKEpbjeTzY2X+GTvUUvfM204iIiMh5AooHv3hhBsNhH1L5Ev7k3QfIVGap7MXJc/FkWTKLfMWSXt1k40YSUccw6pOIekWWJXz05Bh++okpKB4JC1tZ/Ic372M95bzoT5WHkWo6/qr1FDHfL8z5fpbgO/kefHs8UHvl3mYGxZKOaMCL2aFgz78/ERE9LOL34txMDADwxt2tfT9WnKLnBhQRERH1SsjnxS9emMFAUEE8q+I/v1//xLyTZ/wBD4/lYIGCqPNEQd3Nm9dE1FunJ6P4m8/NIRZUkMip+E9vL+JGJQ3PKZj+VC381TZSmfP9Aiz8WcG9j8Z9mBVqCzr+rq0kAQBnpwYgSVxoERHZwTNHhuCRJTzYyWEpXr/rjxtQREREZIVoQMEvXZhBxO/FZqqAP31/yTyQVKugOjfqE6jeq2eK2iN/RkTt45wqIrLCeDSAX33hEA6PhKBqBr5+aQXfvrq251rGjsz4cRfvBfn2qKekCuUI+gg7/izh3kfjPqyK+swVNcxvZgEw5pOIyE4GAgoemxoAUJ71V49Y4Dj1FD0RERE512DIh79xYQYBxYPVRB5//O4D7GSKD32M09cq4l5dxJnKkgSvzAOzRJ1SdHhXMBE5V0Dx4BeemsFzR4YBAJeWEvi3P17A/a2sxVd2MJXzUWvqKYb5Z6Ljj4U/a7j30biPvTJpe+HGWgq6YWB8wI+RiL+n35uIiPb33JFhyJKE+c0sVhN7Z86z44+IiIisNBrx4xcvzCDo82A9WcB/eOs+ri4nzb8vqGIesTPXKmbHX6Xwp3glJuUQdVCRHX9EZCFZlvATJ0fxhWdmMRBUyvOL33uA71xfs2QkV6PYLY2H5jALYsZfhFGflnDvo3EfVnX8Xa/EfJ6ZHOjp9yUiooPFQgpOV7qx36zT9VdgrjsRERFZbGIggL/9wiHMDgVRLOn46yur+MblVRRKmuPXKqILKVMsbyQ59ecgsiPDMKqb1zzISEQWmhsO4b948TCenIsBAD5YTODfvrGAxW17dv9VD4G79zDSXjP+xEGtqF+x5Jrcju/ke/B5qq2pum4c8NGdsZMpYiWRhyQx5pOIyK6ePzoMSQLubmSwlny4688wjGo0jkNP0RMREVF/KM/8m8VLx0cgSeVZ8v/xzfvmrBy/4uwZf+lC+edgygJR56iaAaOyBcaiOhFZzeeV8ckzE/ilC7OIBrxI5lT88bsP8N3r67br/itW4i3d3PG3VyNVih1/lnLvo3EfojUVeHggZTddX00BAA6PhBBm7i0RkS0Nh33m4Yzv39iAYVQPhxRqFp68USYiIiKrybKEF4+N4IvPziEa8GInq5qb+k6d3/VI1CfXXEQdU7tZW7svRkRkpUMjIfwXLx3GEzPl7r+Li3H82zfsNftP5dgXcx9M1FIKJc2MmA/7nXngzOnc+2jch9cjw1MZEN6LuE/DMHB9lTGfRERO8JETo1A8EpbiOdxYS5l/LhY3Hlky30OIiIiIrDYzGMSXXjyM4+MRAEBA8cDr0LWK2FAThT8etiLqHLH/5fPKnJ1JRLbi93rw6ccm8IsXZjAQVJDMqfiT9x7gW1fXkFc1qy+PM/5QjYgulnQYhoFMTTqD38vCnxXc+2g8gHii9qJ1eCWRRzyrwueVcXws0vXvR0RErYsGFDx3ZBgA8NqtTfN9oljijTIRERHZU0Dx4PPnp/D5J6fxc09NO3atIrqQxGYS55CRHfzg5gbevLtlRuk6VdHhM0CJqP8dHgnjSy8ewlNzgwCAy0sJ/Ls3FnBvM2PpdYnXT1cX/iprNMMASrqBdL4y348xn5Zx76PxANVc2u7P+BPdfsfHIq5uCSYicooLh4cwEFSQypfwzsI2AN4oExERkb1JkoQT4xHMDAatvpSWiYhSvZJZynUXWS2RU/H+/Thev7OF/+21eUcXAItmx4ozDwYQkTv4vR584sw4vvDMLAZD5X2ZP3t/CX91aQWpvNrz69F1AyWd65Lan13VdKQK5X+LCEeaWca9j8YD+CoLnW53/Gm6gRuraQDA2aloV78XERF1huKR8fFTowCAd+d3kMipD3X8EREREVHn7T5J7/OyQEHWivq9+Oy5SQyHfcirmlkAfOvetuMKgOLgOztpicgJ5oZD+NKLh3Hh8BAkCbi+msK/fn0eb9zd6snoLqFY873cvB8kSVK1kapU7fhj4c86/M3XoewaSNkt9zYzyKsaIn4v5oZCXf1eRETUOcfHIpgbDmFxO4sf3trAqYny4Q2/ixd6RERERN20u/Dn5kgtsgdZlnB6MoqT4xHcXE/hzbvb2M4U8aPbm3jv/g7OTccgy+WimlrSoWo6VN2ABODpQ4OYtdE+EBNMiMhpyoeyx3BmMorv39jAUjyHH9/ZwuWlBD56cgynJiJdjzcXRUaPLMHj0BnKnaJ4JBRLQEHTkK7MY44w6tMy/M3XUY367F7hzzAMXFspx3yenoxCdvmLAxGRk0iShI+fGsO/f3MBt9bS8FQWk24+4UVERETUTbvXWSxQkF3IsoQzkwM4NR7FjbUU3ry7hZ2sirfnt+t+zp2NNF4+PornjgzZYu6m2P/i/QwROc3EQABffHYWN9fS+OGtDaTyJXz90go+WAzi46fHMDEQ6Nr35ny/qvLvQIOqGWbhL+pXrL0oF2Phrw6z468LUZ/Fko5rK0l8+CCOzXQRAHCGMZ9ERI4zFvXjydlBXFyM4/pqCgA7/oiIiIi6ZXehj5GEZDeyLOHs1ABOT0RxfTWFBztZKB4ZXo8ExSND8cjweWQsxbO4tpLCj25vYjmew2cen0TQ57H02qsz/vi8IiLnkaRyB/axsTDeXdjBO/PbWIrn8B/fuo/zszG8fHwUAaXzr7NmTDLno5rvH2pJZ8efDfA3X4f5QO1gx99WuoAPHyRwdSVZcxpAwjOHhzEW8Xfs+xARUe+8dHwE11dTyKvlGR48IUtERETUHez4I6eQZQmPTQ/gsemBPf/+3MwAZodC+O71ddzbzODfv7mAn35i6v/f3r0GV1ndexz/7ftOyD0h2blCiCmRS5SbEdByTmFkKKe1Yu3xFDvM9EWnbZyCzrR12rG+6FjUTt9oO9r2RXumQqmcyrF4Wk85YNMy5RJAVG4BSySQkMRAwg65kE32Oi9iNkQJRXJ5yLO+n5mM+OwnyZr9z9755fk/ay0VZCSN80ivYNYKADcI+Ly6e1q2ZhakaeeJNh1r7tTbpy/ovdaLurd8YFnQ0ZxlPdg74CbwoSsoDu7xNynk7E0tNqPxN4zBF+tozPi70BPT9qMtOnWuO3EsMzmgyuIMzchPG5O7DQAA4yMc8GlhWbbePNYqSQr6eE8HAAAYCx9tSHDDFSYqj8ejWYXpyksL63/eaVJ7d0yb953RPeXZmlvizNKfLPUJwE1SwwGtmJ2vWYXp2nGsVee7+vTGoWYdborqMxW5ypoUHJXvw2zpKwZvyOqJ9au7b+DmeJb6dA4/kcNILPU5whl/9W1d2rinQafOdcvjkcpyU7RqbqHWLJqquSWZNP0AwAUqC9OVkzowc9vpJXoAAADcihl/cJvJqSH9R1WJpkdSFTdGfz3epv8+2KjO3ti4jyXWf2VlKgBwi+KsZK2uKtGismz5vR6dPt+tl3ef0t/fa9PlUVjpj9nSVww+B+3dA7/D/F6PwgGeF6cw428YV6ammpv6/HjcaPfJc9pTP7CRcyQ9rBWzIspIHp27CQAAtw6v16PPVebrcFNUM/KvvZwPAAAARuajDQn2+IMbhPw+rZgVUVFmkmrqPtD7bd36ze5T+nT5ZM0sSBu32X99lweuf9FQB+A2fp9XVdOyVRFJ05t1A0ss76k/r/pzXfrsrHxljmD2X+KmCTJJIqed77okaWB/PydmsGMAP5HDGPxBvZmlPrv7LuvVtxoTTb87izP00Lwimn4A4GIZyUEtvi2HGX8AAABj5KMNCWYmwS08Ho8qizL05aoSRdLDuhSLa9uRFr12sGncZv/1sdQnAJdLTw7o/jsLtLIyX+GAT63RS9q4t0FHz0Zv+msmlknmponE74/zXQO/t1JCzDlzEj+Rwxicmhr7hFN+Gzt6tGF3g06f71bQ79WK2RH9a0Wu/Lz4AQAAAAC4aR6PZ0hTIsTeynCZ7JSQ/n1+se4tz5Hf61F9W5d+s/uUDjddkDE3tyLVjYqxXB0AC3g8Hn0qL1WP3F2iwswk9V2O641DzXrjUPNNTQBKzJb2czPSYPNz8IaV1DCNPyfx23wYoQ//mPgkL/hDjRf0X/vO6OKly8pOCerhBcWqiLDkGwAAAAAAo+HqWX4BLrLBhbxej+ZPzRoy++/Ph1u0ef8Z1TV3jsqeVNdyZY8/LhUCcL/UcEBfnFuku6dly+ORjp6NauOeU2qN9n6ir9PHe2fC4HKng/eppIQCDo4GtF2HMfhi7bvBQHWgoV01dR9IkioiqVp6ex7LIwAAAAAAMIqCPq+61J/4N+BWg7P/9je0a9c/zqmxvUeN7T1KCvo0Iz9NswvTR7Qv1UcNXv8KcS0LgCW8Xo8WlmWrOCtJbxxqVnt3TJtqT+vTn5qsO4rSb2h/OmZLX/HR5yCFGX+O4tkfxmDT7p8t9WmM0Z7689r1j3OSpHlTMnVveQ4bVwIAAAAAMMoG7yb3ejzyefm7G+7m9Xq0YGqWpkdSdajxgo40RdXZe1n7T7Vr/6l2FWUmaUZBmqZmT9KkEe6l1MfFawCWKspM1uqqKdp2tEX/aL2oN4+1qvlCjz5T8c8n9sTYHzXhozdkpYRYkt1JNP6GkZjxd52lPo0x+uuJNh041S5JWliWrarSLJp+AAAAAACMgcG/1QN+D397wxpp4YAWleXo7tJs1Z/r0qHGC6pv69KZ9h6dae+RJOWkhjQlK1lTspNVkJH0iRt4sf6BtdmuXk4XAGyRFPTpc5X5OtDQoZ0n2nT0bKc+6Lykf6ssuO7s6sHZ0qxC8PF9Dlnq01k0/oYx+GIdbsZfPG60/VirDjVekCQtmT5Zc0syx218AAAAAADYZnAZQi6wwUZer0dlk1NUNjlF0d6YDjdGdbLtolqjl9TWOfCx/1S7/F6PirKStLgsR7lp4X/6dY0xiRvfmbUCwFYej0fzpmQqLy2kP757Vm0X+7Rxb4OWz4zottyUa37OlZsmeO9kqc9bC8/+MK4s9WkUjxt5r1pCpD9u9L+Hm1XX3CmPR1p2e55mFaY7NVQAAAAAAKwweFGJ5gRslxYOaGFZthaWZau777JOn+/RqXNdajjfrc7ey3q/rVsN505r3pRMVU3Luu5F6f64Udxw8RoApIGlP79cNUV/fOesGjt6tPXtJi2YmqVFZdlDegTS1cskM1v66t8fXo9HyQGW+nQSjb9hXP1i7euPK+z1qT9udKw5qtr682rvjsnn9WjFrIjK81IdHCkAAAAAAHYYnOnHjD/giuSgX9MjqZoeSZUxRu3dMe0+eU51zZ2qff+83mvt1LIZeSrKTL7m5w/OWJF4bQGAJKWE/HpwXpH+duIDvdXQodr3z6vt4iWtrMwf0uBij78rrn4OJoV8H2uSYnzxEzkMv8+b2Cj8Uiyud89c0H/+/X39+XCL2rtjCgd8+vwdBTT9AAAAAAAYJ4EPLyoxKwm4No/Ho6xJQX12dr4+f2eBUkJ+tXfHtHnfGW0/2qJLl/s/9jlXz1jhQi0ADPB5PfqX6bkfNvs8qm/r0pa3GtUbu/I+GmOPv4Srs1kqy3w6jgpcR8DnVX+8Xxv3NiRe0MlBn+ZOyVRlUbpCfqarAgAAAAAwXoIs9QncsLLJKSrMSNLOE216t/GC3jlzQfVtXbqzOEPlualKTw5IGljpSqKhDgDX8qm8VE0K+fXawUY1tvfo9wfO6IE5hUoO+nn/vMrVzc+UUMDBkUCi8XddAZ9HvTGpN9avlJBfc6dkanZhOn9gAAAAAADggJSQf8h/AVxfOODTshl5mh5J1f8dbVFHd0x/O9Gmv51oU15aWOV5KYnXExeuAeDaCjOS9MV5RdpyoFGt0UvavO+MHphbeGXGNP2CIVunTQoxYcppJOXrqIik6WTbRVUWZWhWQZr8BCAAAAAAABxTkZ8qn9ejqTnX3qsMwLUVZyXrkbun6OjZqE60XNTp9m61RHvVEu1NnMOFawAYXm5qWF+aX6zfHzij8119eqX2tMyHW6Sy1OeVrdP644alPm8BVOA67inP0T3lOU4PAwA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      "text/plain": [
       "<Figure size 1800x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.MVP.show_preds(sharey=True) # these preds are highly inaccurate as the model's been trained for just 1 epoch for testing purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.773015</td>\n",
       "      <td>0.744267</td>\n",
       "      <td>0.460863</td>\n",
       "      <td>00:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fine-tune\n",
    "tfms  = [None, [TSCategorize()]]\n",
    "batch_tfms = [TSStandardize(by_var=True), TSNan2Value()]\n",
    "labeled_dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms, bs=64)\n",
    "learn = ts_learner(labeled_dls, InceptionTimePlus, pretrained=True, weights_path=f'models/MVP/{dsid}.pth', metrics=accuracy)\n",
    "learn.fit_one_cycle(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nacho/opt/anaconda3/envs/py37torch113/lib/python3.7/site-packages/ipykernel_launcher.py:42: UserWarning: Only future_mask will be used\n"
     ]
    }
   ],
   "source": [
    "tfms  = [None, [TSCategorize()]]\n",
    "batch_tfms = [TSStandardize(by_var=True), TSNan2Value()]\n",
    "unlabeled_dls = get_ts_dls(X, splits=splits, tfms=tfms, batch_tfms=batch_tfms, bs=64)\n",
    "fname = f'{dsid}_test'\n",
    "mvp = MVP(subsequence_mask=True, sync='random', variable_mask=True, future_mask=True, fname=fname)\n",
    "learn = ts_learner(unlabeled_dls, InceptionTimePlus, metrics=accuracy, cbs=mvp) # Metrics will not be used!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nacho/opt/anaconda3/envs/py37torch113/lib/python3.7/site-packages/ipykernel_launcher.py:40: UserWarning: Only custom_mask will be used\n"
     ]
    }
   ],
   "source": [
    "tfms  = [None, [TSCategorize()]]\n",
    "batch_tfms = [TSStandardize(by_var=True)]\n",
    "unlabeled_dls = get_ts_dls(X, splits=splits, tfms=tfms, batch_tfms=batch_tfms, bs=64)\n",
    "fname = f'{dsid}_test'\n",
    "mvp = MVP(subsequence_mask=True, sync='random', variable_mask=True, future_mask=True, custom_mask=partial(create_future_mask, r=.15),\n",
    "                fname=fname)\n",
    "learn = ts_learner(unlabeled_dls, InceptionTimePlus, metrics=accuracy, cbs=mvp) # Metrics will not be used!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: os.remove(\"models/MVP/MoteStrain.pth\")\n",
    "except OSError: pass\n",
    "try: os.remove(\"models/MVP/model.pth\")\n",
    "except OSError: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "IPython.notebook.save_checkpoint();",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/nacho/notebooks/tsai/nbs/027_callback.MVP.ipynb saved at 2023-02-17 19:39:04\n",
      "Correct notebook to script conversion! 😃\n",
      "Friday 17/02/23 19:39:07 CET\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|eval: false\n",
    "#|hide\n",
    "from tsai.export import get_nb_name; nb_name = get_nb_name(locals())\n",
    "from tsai.imports import create_scripts; create_scripts(nb_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
